{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三阶段 - 第3讲：缺失值与异常值处理\n",
    "\n",
    "## 学习目标\n",
    "- 深入理解缺失值的产生原因和影响\n",
    "- 掌握缺失值的检测和可视化方法\n",
    "- 熟练使用多种缺失值处理策略\n",
    "- 掌握异常值的多种检测方法（业务规则、统计法、IQR法）\n",
    "- 学会根据场景选择合适的异常值处理方式\n",
    "- 完成真实数据的清洗实战\n",
    "\n",
    "**重要性**: ⭐⭐⭐⭐⭐ 数据清洗的核心技能！\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 环境配置完成\n",
      "Pandas版本: 2.2.3\n",
      "Numpy版本: 2.2.5\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "\n",
    "# 中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "print(\"✅ 环境配置完成\")\n",
    "print(f\"Pandas版本: {pd.__version__}\")\n",
    "print(f\"Numpy版本: {np.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、缺失值深度解析\n",
    "\n",
    "### 1.1 什么是缺失值？\n",
    "\n",
    "**定义**: 数据集中某些观测值没有被记录的现象\n",
    "\n",
    "**表现形式**:\n",
    "- Python/Pandas: `NaN` (Not a Number), `None`, `pd.NA`\n",
    "- Excel: 空白单元格\n",
    "- 数据库: `NULL`\n",
    "- 其他: 特殊标记如 `9999`, `-1`, `N/A`, `unknown`\n",
    "\n",
    "### 1.2 缺失值的产生原因\n",
    "\n",
    "1. **数据采集问题**\n",
    "   - 传感器故障\n",
    "   - 网络中断\n",
    "   - 人工录入遗漏\n",
    "\n",
    "2. **用户行为**\n",
    "   - 表单未填写完整\n",
    "   - 拒绝提供某些信息\n",
    "   - 问卷跳题逻辑\n",
    "\n",
    "3. **数据处理**\n",
    "   - 数据合并时匹配不上\n",
    "   - 数据转换错误\n",
    "   - 系统bug\n",
    "\n",
    "4. **数据设计**\n",
    "   - 某些字段本身可选\n",
    "   - 不同版本数据结构不同\n",
    "\n",
    "### 1.3 缺失值的影响\n",
    "\n",
    "❌ **负面影响**:\n",
    "- 统计结果偏差\n",
    "- 模型无法训练\n",
    "- 分析结论错误\n",
    "- 数据量减少\n",
    "\n",
    "✅ **潜在信息**:\n",
    "- 缺失本身可能有意义（如收入不愿透露）\n",
    "- 缺失模式可能揭示问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 二、缺失值的检测\n",
    "\n",
    "### 2.1 创建包含缺失值的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 创建了包含多种缺失模式的员工数据集\n",
      "数据规模: (50, 12)\n",
      "\n",
      "前10行数据:\n",
      "  employee_id  name  age department position   salary  years_of_service  \\\n",
      "0       E0001   员工1   50        技术部       经理 49020.00                11   \n",
      "1       E0002   员工2   36      人力资源部       专员  7693.00                 5   \n",
      "2       E0003   员工3   29        财务部       经理 34467.00                 3   \n",
      "3       E0004   员工4   42        技术部       专员 34723.00                10   \n",
      "4       E0005   员工5   40        技术部       主管  8627.00                16   \n",
      "5       E0006   员工6   44      人力资源部       专员 30939.00                 5   \n",
      "6       E0007   员工7   32        技术部       经理      NaN                 4   \n",
      "7       E0008   员工8   32        技术部       经理 26834.00                19   \n",
      "8       E0009   员工9   45      人力资源部       主管 23047.00                 1   \n",
      "9       E0010  员工10   57      人力资源部       专员      NaN                 5   \n",
      "\n",
      "   performance_score                   email        phone city  bonus  \n",
      "0               5.00   employee1@company.com  13897692768   上海    NaN  \n",
      "1               5.00   employee2@company.com  13843255876   广州    NaN  \n",
      "2               5.00   employee3@company.com  13859114107   上海    NaN  \n",
      "3               4.00   employee4@company.com  13825900476   北京    NaN  \n",
      "4               3.00   employee5@company.com  13850820227   杭州    NaN  \n",
      "5               5.00   employee6@company.com  13889688895  NaN    NaN  \n",
      "6               3.00   employee7@company.com  13897927856   上海    NaN  \n",
      "7               4.00   employee8@company.com          NaN   北京    NaN  \n",
      "8               4.00   employee9@company.com  13899595920   深圳    NaN  \n",
      "9               5.00  employee10@company.com  13822707035   杭州    NaN  \n"
     ]
    }
   ],
   "source": [
    "# 创建一个真实场景的数据集 - 员工信息\n",
    "np.random.seed(42)\n",
    "n = 50\n",
    "\n",
    "# 基础数据\n",
    "employee_data = {\n",
    "    'employee_id': [f'E{str(i).zfill(4)}' for i in range(1, n+1)],\n",
    "    'name': [f'员工{i}' for i in range(1, n+1)],\n",
    "    'age': np.random.randint(22, 60, n),\n",
    "    'department': np.random.choice(['销售部', '技术部', '市场部', '人力资源部', '财务部'], n),\n",
    "    'position': np.random.choice(['经理', '主管', '专员', '助理'], n),\n",
    "    'salary': np.random.randint(5000, 50000, n),\n",
    "    'years_of_service': np.random.randint(0, 20, n),\n",
    "    'performance_score': np.random.choice([3, 4, 5], n),\n",
    "    'email': [f'employee{i}@company.com' for i in range(1, n+1)],\n",
    "    'phone': [f'138{str(np.random.randint(10000000, 99999999))}' for i in range(n)],\n",
    "    'city': np.random.choice(['北京', '上海', '广州', '深圳', '杭州'], n)\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(employee_data)\n",
    "\n",
    "# 人为引入缺失值（模拟真实场景）\n",
    "\n",
    "# 1. 完全随机缺失 (MCAR - Missing Completely At Random)\n",
    "# 电话号码有10%随机缺失\n",
    "missing_indices = np.random.choice(df.index, size=int(n*0.1), replace=False)\n",
    "df.loc[missing_indices, 'phone'] = np.nan\n",
    "\n",
    "# 2. 随机缺失 (MAR - Missing At Random)\n",
    "# 年龄较大的员工（>50岁）有30%不愿透露邮箱\n",
    "older_employees = df[df['age'] > 50].index\n",
    "missing_email = np.random.choice(older_employees, size=int(len(older_employees)*0.3), replace=False)\n",
    "df.loc[missing_email, 'email'] = np.nan\n",
    "\n",
    "# 3. 非随机缺失 (MNAR - Missing Not At Random)\n",
    "# 高薪员工（>30000）有50%不愿透露薪资\n",
    "high_salary = df[df['salary'] > 30000].index\n",
    "missing_salary = np.random.choice(high_salary, size=int(len(high_salary)*0.5), replace=False)\n",
    "df.loc[missing_salary, 'salary'] = np.nan\n",
    "\n",
    "# 4. 结构性缺失\n",
    "# 新员工（工作年限<1年）没有绩效评分\n",
    "df.loc[df['years_of_service'] < 1, 'performance_score'] = np.nan\n",
    "\n",
    "# 5. 一些随机缺失\n",
    "random_missing = np.random.choice(df.index, size=5, replace=False)\n",
    "df.loc[random_missing, 'city'] = np.nan\n",
    "\n",
    "# 添加一列全是缺失值（测试用）\n",
    "df['bonus'] = np.nan\n",
    "\n",
    "print(\"✅ 创建了包含多种缺失模式的员工数据集\")\n",
    "print(f\"数据规模: {df.shape}\")\n",
    "print(\"\\n前10行数据:\")\n",
    "print(df.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 基础检测方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 缺失值检测 ===\n",
      "\n",
      "各列缺失值数量:\n",
      "employee_id           0\n",
      "name                  0\n",
      "age                   0\n",
      "department            0\n",
      "position              0\n",
      "salary               11\n",
      "years_of_service      0\n",
      "performance_score     4\n",
      "email                 2\n",
      "phone                 5\n",
      "city                  5\n",
      "bonus                50\n",
      "dtype: int64\n",
      "\n",
      "各列缺失值占比:\n",
      "employee_id           0.00\n",
      "name                  0.00\n",
      "age                   0.00\n",
      "department            0.00\n",
      "position              0.00\n",
      "salary               22.00\n",
      "years_of_service      0.00\n",
      "performance_score     8.00\n",
      "email                 4.00\n",
      "phone                10.00\n",
      "city                 10.00\n",
      "bonus               100.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 方法1: isnull() / isna() - 检测缺失值（两者等价）\n",
    "print(\"=== 缺失值检测 ===\")\n",
    "print(\"\\n各列缺失值数量:\")\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print(\"\\n各列缺失值占比:\")\n",
    "missing_pct = (df.isnull().sum() / len(df) * 100).round(2)\n",
    "print(missing_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 使用info()查看缺失情况 ===\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   employee_id        50 non-null     object \n",
      " 1   name               50 non-null     object \n",
      " 2   age                50 non-null     int64  \n",
      " 3   department         50 non-null     object \n",
      " 4   position           50 non-null     object \n",
      " 5   salary             39 non-null     float64\n",
      " 6   years_of_service   50 non-null     int64  \n",
      " 7   performance_score  46 non-null     float64\n",
      " 8   email              48 non-null     object \n",
      " 9   phone              45 non-null     object \n",
      " 10  city               45 non-null     object \n",
      " 11  bonus              0 non-null      float64\n",
      "dtypes: float64(3), int64(2), object(7)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# 方法2: info() - 快速查看\n",
    "print(\"\\n=== 使用info()查看缺失情况 ===\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 缺失值汇总报告 ===\n",
      "                   总数  占比(%)     数据类型\n",
      "bonus              50 100.00  float64\n",
      "salary             11  22.00  float64\n",
      "phone               5  10.00   object\n",
      "city                5  10.00   object\n",
      "performance_score   4   8.00  float64\n",
      "email               2   4.00   object\n"
     ]
    }
   ],
   "source": [
    "# 方法3: 缺失值汇总报告\n",
    "def missing_summary(df):\n",
    "    \"\"\"\n",
    "    生成缺失值汇总报告\n",
    "    \"\"\"\n",
    "    total = df.isnull().sum()\n",
    "    percent = (df.isnull().sum() / len(df) * 100)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['总数', '占比(%)'])\n",
    "    missing_data = missing_data[missing_data['总数'] > 0].sort_values('总数', ascending=False)\n",
    "    missing_data['占比(%)'] = missing_data['占比(%)'].round(2)\n",
    "    missing_data['数据类型'] = df.dtypes\n",
    "    return missing_data\n",
    "\n",
    "print(\"\\n=== 缺失值汇总报告 ===\")\n",
    "print(missing_summary(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 行级缺失统计 ===\n",
      "完整行（无缺失）: 0 行\n",
      "至少1个缺失: 50 行\n",
      "至少2个缺失: 19 行\n",
      "最多缺失数: 4 列\n"
     ]
    }
   ],
   "source": [
    "# 方法4: 检查每行的缺失情况\n",
    "print(\"\\n=== 行级缺失统计 ===\")\n",
    "row_missing = df.isnull().sum(axis=1)\n",
    "print(f\"完整行（无缺失）: {(row_missing == 0).sum()} 行\")\n",
    "print(f\"至少1个缺失: {(row_missing > 0).sum()} 行\")\n",
    "print(f\"至少2个缺失: {(row_missing > 1).sum()} 行\")\n",
    "print(f\"最多缺失数: {row_missing.max()} 列\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 缺失值可视化\n",
    "\n",
    "**Excel对比**: Excel需要手动条件格式高亮，Pandas可以直接可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化1: 缺失值热力图\n",
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "\n",
    "# 左图: 缺失值位置热力图\n",
    "sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis', ax=axes[0])\n",
    "axes[0].set_title('Missing Values Heatmap (Yellow = Missing)', fontsize=14, fontweight='bold')\n",
    "axes[0].set_xlabel('Columns')\n",
    "\n",
    "# 右图: 各列缺失值统计柱状图\n",
    "missing_counts = df.isnull().sum()\n",
    "missing_counts = missing_counts[missing_counts > 0].sort_values(ascending=True)\n",
    "missing_counts.plot(kind='barh', ax=axes[1], color='coral', edgecolor='black')\n",
    "axes[1].set_title('Missing Values Count by Column', fontsize=14, fontweight='bold')\n",
    "axes[1].set_xlabel('Number of Missing Values')\n",
    "axes[1].grid(axis='x', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化2: 缺失值矩阵（展示前30行）\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(df.head(30).isnull(), cbar=True, cmap='coolwarm', \n",
    "            yticklabels=True, xticklabels=True)\n",
    "plt.title('Missing Values Matrix (First 30 Rows)', fontsize=14, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 缺失值模式分析\n",
    "\n",
    "**重要**: 理解缺失模式有助于选择正确的处理策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 缺失值模式分析 ===\n",
      "\n",
      "1. 薪资缺失情况分析:\n",
      "有薪资数据的平均年龄: 38.9\n",
      "缺失薪资的平均年龄: 38.5\n",
      "\n",
      "按职位统计薪资缺失:\n",
      "position\n",
      "专员    5\n",
      "主管    2\n",
      "助理    2\n",
      "经理    2\n",
      "Name: salary, dtype: int64\n",
      "\n",
      "2. 绩效评分缺失与工作年限:\n",
      "有绩效评分的平均工作年限: 10.0年\n",
      "缺失绩效评分的平均工作年限: 0.0年\n",
      "\n",
      "3. 是否缺失salary与其他变量的关系:\n",
      "age                -0.02\n",
      "years_of_service   -0.15\n",
      "Name: salary_missing, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 分析缺失值模式\n",
    "print(\"=== 缺失值模式分析 ===\")\n",
    "\n",
    "# 1. 薪资缺失与年龄、职位的关系\n",
    "print(\"\\n1. 薪资缺失情况分析:\")\n",
    "salary_missing = df['salary'].isnull()\n",
    "print(f\"有薪资数据的平均年龄: {df[~salary_missing]['age'].mean():.1f}\")\n",
    "print(f\"缺失薪资的平均年龄: {df[salary_missing]['age'].mean():.1f}\")\n",
    "\n",
    "print(\"\\n按职位统计薪资缺失:\")\n",
    "position_missing = df.groupby('position')['salary'].apply(lambda x: x.isnull().sum())\n",
    "print(position_missing)\n",
    "\n",
    "# 2. 绩效评分缺失与工作年限的关系\n",
    "print(\"\\n2. 绩效评分缺失与工作年限:\")\n",
    "perf_missing = df['performance_score'].isnull()\n",
    "print(f\"有绩效评分的平均工作年限: {df[~perf_missing]['years_of_service'].mean():.1f}年\")\n",
    "print(f\"缺失绩效评分的平均工作年限: {df[perf_missing]['years_of_service'].mean():.1f}年\")\n",
    "\n",
    "# 3. 相关性分析\n",
    "print(\"\\n3. 是否缺失salary与其他变量的关系:\")\n",
    "df['salary_missing'] = df['salary'].isnull().astype(int)\n",
    "correlations = df[['salary_missing', 'age', 'years_of_service']].corr()['salary_missing'].drop('salary_missing')\n",
    "print(correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化缺失模式\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# 左图: 年龄分布 - 按薪资是否缺失分组\n",
    "df[~df['salary'].isnull()]['age'].hist(bins=15, alpha=0.5, label='Has Salary', ax=axes[0], color='blue')\n",
    "df[df['salary'].isnull()]['age'].hist(bins=15, alpha=0.5, label='Missing Salary', ax=axes[0], color='red')\n",
    "axes[0].set_title('Age Distribution by Salary Missing Status', fontweight='bold')\n",
    "axes[0].set_xlabel('Age')\n",
    "axes[0].set_ylabel('Frequency')\n",
    "axes[0].legend()\n",
    "axes[0].grid(alpha=0.3)\n",
    "\n",
    "# 右图: 工作年限分布 - 按绩效评分是否缺失分组\n",
    "df[~df['performance_score'].isnull()]['years_of_service'].hist(bins=10, alpha=0.5, label='Has Score', ax=axes[1], color='green')\n",
    "df[df['performance_score'].isnull()]['years_of_service'].hist(bins=10, alpha=0.5, label='Missing Score', ax=axes[1], color='orange')\n",
    "axes[1].set_title('Years of Service by Performance Score Missing', fontweight='bold')\n",
    "axes[1].set_xlabel('Years of Service')\n",
    "axes[1].set_ylabel('Frequency')\n",
    "axes[1].legend()\n",
    "axes[1].grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 三、缺失值处理策略\n",
    "\n",
    "### 3.1 删除法\n",
    "\n",
    "**适用场景**:\n",
    "- 缺失比例很小（<5%）\n",
    "- 缺失是完全随机的\n",
    "- 数据量充足\n",
    "\n",
    "**优点**: 简单直接\n",
    "\n",
    "**缺点**: 损失数据，可能引入偏差\n",
    "\n",
    "**Excel对比**: Excel中需要手动筛选删除，Pandas一行代码搞定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 删除法示例 ===\n",
      "原始数据: (50, 13)\n",
      "\n",
      "1. 删除任何含缺失的行: (0, 13) (删除了50行)\n",
      "2. 删除全部为缺失的行: (50, 13) (删除了0行)\n",
      "3. 至少10个非缺失值: (48, 13) (删除了2行)\n",
      "4. 删除ID/姓名/年龄缺失的行: (50, 13) (删除了0行)\n",
      "5. 删除缺失>50%的列: (50, 12) (删除了1列)\n",
      "   删除的列: {'bonus'}\n"
     ]
    }
   ],
   "source": [
    "# 创建副本用于演示\n",
    "df_drop = df.copy()\n",
    "\n",
    "print(\"=== 删除法示例 ===\")\n",
    "print(f\"原始数据: {df_drop.shape}\")\n",
    "\n",
    "# 方法1: 删除任何包含缺失值的行\n",
    "df_drop1 = df_drop.dropna()\n",
    "print(f\"\\n1. 删除任何含缺失的行: {df_drop1.shape} (删除了{len(df_drop)-len(df_drop1)}行)\")\n",
    "\n",
    "# 方法2: 删除全部为缺失值的行\n",
    "df_drop2 = df_drop.dropna(how='all')\n",
    "print(f\"2. 删除全部为缺失的行: {df_drop2.shape} (删除了{len(df_drop)-len(df_drop2)}行)\")\n",
    "\n",
    "# 方法3: 指定至少有N个非缺失值才保留\n",
    "df_drop3 = df_drop.dropna(thresh=10)  # 至少10个非缺失值\n",
    "print(f\"3. 至少10个非缺失值: {df_drop3.shape} (删除了{len(df_drop)-len(df_drop3)}行)\")\n",
    "\n",
    "# 方法4: 只考虑特定列\n",
    "df_drop4 = df_drop.dropna(subset=['employee_id', 'name', 'age'])\n",
    "print(f\"4. 删除ID/姓名/年龄缺失的行: {df_drop4.shape} (删除了{len(df_drop)-len(df_drop4)}行)\")\n",
    "\n",
    "# 方法5: 删除缺失值超过阈值的列\n",
    "threshold = 0.5  # 缺失超过50%的列\n",
    "df_drop5 = df_drop.dropna(axis=1, thresh=int(len(df_drop) * (1-threshold)))\n",
    "print(f\"5. 删除缺失>50%的列: {df_drop5.shape} (删除了{df_drop.shape[1]-df_drop5.shape[1]}列)\")\n",
    "print(f\"   删除的列: {set(df_drop.columns) - set(df_drop5.columns)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 填充法 - 固定值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 固定值填充 ===\n",
      "1. bonus列用0填充: 0个缺失\n",
      "2. city列用'未知'填充: 0个缺失\n",
      "3. phone列用'暂无联系方式'填充: 0个缺失\n",
      "\n",
      "填充后示例:\n",
      "  employee_id        phone city  bonus\n",
      "0       E0001  13897692768   上海   0.00\n",
      "1       E0002  13843255876   广州   0.00\n",
      "2       E0003  13859114107   上海   0.00\n",
      "3       E0004  13825900476   北京   0.00\n",
      "4       E0005  13850820227   杭州   0.00\n",
      "5       E0006  13889688895   未知   0.00\n",
      "6       E0007  13897927856   上海   0.00\n",
      "7       E0008       暂无联系方式   北京   0.00\n",
      "8       E0009  13899595920   深圳   0.00\n",
      "9       E0010  13822707035   杭州   0.00\n"
     ]
    }
   ],
   "source": [
    "# 固定值填充\n",
    "df_fill = df.copy()\n",
    "\n",
    "print(\"=== 固定值填充 ===\")\n",
    "\n",
    "# 方法1: 填充为0\n",
    "df_fill['bonus'].fillna(0, inplace=True)\n",
    "print(f\"1. bonus列用0填充: {df_fill['bonus'].isnull().sum()}个缺失\")\n",
    "\n",
    "# 方法2: 填充为指定字符串\n",
    "df_fill['city'].fillna('未知', inplace=True)\n",
    "print(f\"2. city列用'未知'填充: {df_fill['city'].isnull().sum()}个缺失\")\n",
    "\n",
    "# 方法3: 填充为特定值（根据业务逻辑）\n",
    "df_fill['phone'].fillna('暂无联系方式', inplace=True)\n",
    "print(f\"3. phone列用'暂无联系方式'填充: {df_fill['phone'].isnull().sum()}个缺失\")\n",
    "\n",
    "print(\"\\n填充后示例:\")\n",
    "print(df_fill[['employee_id', 'phone', 'city', 'bonus']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 填充法 - 统计值填充\n",
    "\n",
    "**最常用的方法！**\n",
    "\n",
    "**选择原则**:\n",
    "- **均值**: 数据近似正态分布，无极端异常值\n",
    "- **中位数**: 有异常值，或数据偏斜\n",
    "- **众数**: 分类变量，或离散数值变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 统计值填充 ===\n",
      "1. 薪资均值填充: 均值=23834\n",
      "   缺失数: 0\n",
      "\n",
      "2. 薪资中位数填充: 中位数=26556\n",
      "   缺失数: 0\n",
      "\n",
      "3. 绩效评分众数填充: 众数=4.0\n",
      "   缺失数: 0\n",
      "\n",
      "=== 填充方法对比 ===\n",
      "        原始数据     均值填充    中位数填充\n",
      "均值  23834.31 23834.31 24433.08\n",
      "中位数 26556.00 23834.31 26556.00\n",
      "标准差 12700.83 11184.74 11242.57\n"
     ]
    }
   ],
   "source": [
    "# 统计值填充\n",
    "df_stat = df.copy()\n",
    "\n",
    "print(\"=== 统计值填充 ===\")\n",
    "\n",
    "# 1. 均值填充\n",
    "salary_mean = df_stat['salary'].mean()\n",
    "df_stat['salary_mean_filled'] = df_stat['salary'].fillna(salary_mean)\n",
    "print(f\"1. 薪资均值填充: 均值={salary_mean:.0f}\")\n",
    "print(f\"   缺失数: {df_stat['salary_mean_filled'].isnull().sum()}\")\n",
    "\n",
    "# 2. 中位数填充\n",
    "salary_median = df_stat['salary'].median()\n",
    "df_stat['salary_median_filled'] = df_stat['salary'].fillna(salary_median)\n",
    "print(f\"\\n2. 薪资中位数填充: 中位数={salary_median:.0f}\")\n",
    "print(f\"   缺失数: {df_stat['salary_median_filled'].isnull().sum()}\")\n",
    "\n",
    "# 3. 众数填充\n",
    "perf_mode = df_stat['performance_score'].mode()[0]\n",
    "df_stat['performance_score_filled'] = df_stat['performance_score'].fillna(perf_mode)\n",
    "print(f\"\\n3. 绩效评分众数填充: 众数={perf_mode}\")\n",
    "print(f\"   缺失数: {df_stat['performance_score_filled'].isnull().sum()}\")\n",
    "\n",
    "# 对比不同填充方法\n",
    "print(\"\\n=== 填充方法对比 ===\")\n",
    "comparison = pd.DataFrame({\n",
    "    '原始数据': [df_stat['salary'].mean(), df_stat['salary'].median(), df_stat['salary'].std()],\n",
    "    '均值填充': [df_stat['salary_mean_filled'].mean(), df_stat['salary_mean_filled'].median(), df_stat['salary_mean_filled'].std()],\n",
    "    '中位数填充': [df_stat['salary_median_filled'].mean(), df_stat['salary_median_filled'].median(), df_stat['salary_median_filled'].std()]\n",
    "}, index=['均值', '中位数', '标准差'])\n",
    "print(comparison.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化不同填充方法的效果\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "# 原始数据分布\n",
    "df_stat['salary'].dropna().hist(bins=20, ax=axes[0], edgecolor='black', alpha=0.7, color='skyblue')\n",
    "axes[0].axvline(df_stat['salary'].mean(), color='red', linestyle='--', linewidth=2, label=f'Mean: {salary_mean:.0f}')\n",
    "axes[0].axvline(df_stat['salary'].median(), color='green', linestyle='--', linewidth=2, label=f'Median: {salary_median:.0f}')\n",
    "axes[0].set_title('Original Salary Distribution', fontsize=12, fontweight='bold')\n",
    "axes[0].set_xlabel('Salary')\n",
    "axes[0].legend()\n",
    "axes[0].grid(alpha=0.3)\n",
    "\n",
    "# 均值填充后\n",
    "df_stat['salary_mean_filled'].hist(bins=20, ax=axes[1], edgecolor='black', alpha=0.7, color='lightcoral')\n",
    "axes[1].axvline(salary_mean, color='red', linestyle='--', linewidth=2, label='Filled Value')\n",
    "axes[1].set_title('After Mean Imputation', fontsize=12, fontweight='bold')\n",
    "axes[1].set_xlabel('Salary')\n",
    "axes[1].legend()\n",
    "axes[1].grid(alpha=0.3)\n",
    "\n",
    "# 中位数填充后\n",
    "df_stat['salary_median_filled'].hist(bins=20, ax=axes[2], edgecolor='black', alpha=0.7, color='lightgreen')\n",
    "axes[2].axvline(salary_median, color='green', linestyle='--', linewidth=2, label='Filled Value')\n",
    "axes[2].set_title('After Median Imputation', fontsize=12, fontweight='bold')\n",
    "axes[2].set_xlabel('Salary')\n",
    "axes[2].legend()\n",
    "axes[2].grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 填充法 - 前向/后向填充\n",
    "\n",
    "**适用场景**: 时间序列数据，相邻值相似\n",
    "\n",
    "**方法**:\n",
    "- `ffill()` / `pad()`: 前向填充（用前一个值）\n",
    "- `bfill()` / `backfill()`: 后向填充（用后一个值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 前向/后向填充 ===\n",
      "\n",
      "原始数据:\n",
      "        date  temperature\n",
      "0 2024-01-01        15.00\n",
      "1 2024-01-02        16.00\n",
      "2 2024-01-03          NaN\n",
      "3 2024-01-04          NaN\n",
      "4 2024-01-05        19.00\n",
      "5 2024-01-06        20.00\n",
      "6 2024-01-07          NaN\n",
      "7 2024-01-08        22.00\n",
      "8 2024-01-09        23.00\n",
      "9 2024-01-10          NaN\n",
      "\n",
      "前向填充（用前一天的温度）:\n",
      "        date  temperature  temp_ffill\n",
      "0 2024-01-01        15.00       15.00\n",
      "1 2024-01-02        16.00       16.00\n",
      "2 2024-01-03          NaN       16.00\n",
      "3 2024-01-04          NaN       16.00\n",
      "4 2024-01-05        19.00       19.00\n",
      "5 2024-01-06        20.00       20.00\n",
      "6 2024-01-07          NaN       20.00\n",
      "7 2024-01-08        22.00       22.00\n",
      "8 2024-01-09        23.00       23.00\n",
      "9 2024-01-10          NaN       23.00\n",
      "\n",
      "后向填充（用后一天的温度）:\n",
      "        date  temperature  temp_bfill\n",
      "0 2024-01-01        15.00       15.00\n",
      "1 2024-01-02        16.00       16.00\n",
      "2 2024-01-03          NaN       19.00\n",
      "3 2024-01-04          NaN       19.00\n",
      "4 2024-01-05        19.00       19.00\n",
      "5 2024-01-06        20.00       20.00\n",
      "6 2024-01-07          NaN       22.00\n",
      "7 2024-01-08        22.00       22.00\n",
      "8 2024-01-09        23.00       23.00\n",
      "9 2024-01-10          NaN         NaN\n"
     ]
    }
   ],
   "source": [
    "# 创建时间序列示例\n",
    "time_series = pd.DataFrame({\n",
    "    'date': pd.date_range('2024-01-01', periods=10, freq='D'),\n",
    "    'temperature': [15, 16, np.nan, np.nan, 19, 20, np.nan, 22, 23, np.nan]\n",
    "})\n",
    "\n",
    "print(\"=== 前向/后向填充 ===\")\n",
    "print(\"\\n原始数据:\")\n",
    "print(time_series)\n",
    "\n",
    "# 前向填充\n",
    "time_series['temp_ffill'] = time_series['temperature'].fillna(method='ffill')\n",
    "print(\"\\n前向填充（用前一天的温度）:\")\n",
    "print(time_series)\n",
    "\n",
    "# 后向填充\n",
    "time_series['temp_bfill'] = time_series['temperature'].fillna(method='bfill')\n",
    "print(\"\\n后向填充（用后一天的温度）:\")\n",
    "print(time_series[['date', 'temperature', 'temp_bfill']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 填充法 - 插值法\n",
    "\n",
    "**适用场景**: 数值型连续变量，需要更平滑的填充\n",
    "\n",
    "**方法**:\n",
    "- `linear`: 线性插值（默认）\n",
    "- `polynomial`: 多项式插值\n",
    "- `spline`: 样条插值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 插值法 ===\n",
      "\n",
      "不同插值方法对比:\n",
      "        date  temperature  temp_ffill  temp_linear  temp_poly\n",
      "0 2024-01-01        15.00       15.00        15.00      15.00\n",
      "1 2024-01-02        16.00       16.00        16.00      16.00\n",
      "2 2024-01-03          NaN       16.00        17.00      17.00\n",
      "3 2024-01-04          NaN       16.00        18.00      18.00\n",
      "4 2024-01-05        19.00       19.00        19.00      19.00\n",
      "5 2024-01-06        20.00       20.00        20.00      20.00\n",
      "6 2024-01-07          NaN       20.00        21.00      21.00\n",
      "7 2024-01-08        22.00       22.00        22.00      22.00\n",
      "8 2024-01-09        23.00       23.00        23.00      23.00\n",
      "9 2024-01-10          NaN       23.00        23.00        NaN\n"
     ]
    }
   ],
   "source": [
    "# 插值法示例\n",
    "print(\"=== 插值法 ===\")\n",
    "\n",
    "# 线性插值\n",
    "time_series['temp_linear'] = time_series['temperature'].interpolate(method='linear')\n",
    "\n",
    "# 多项式插值\n",
    "time_series['temp_poly'] = time_series['temperature'].interpolate(method='polynomial', order=2)\n",
    "\n",
    "print(\"\\n不同插值方法对比:\")\n",
    "print(time_series[['date', 'temperature', 'temp_ffill', 'temp_linear', 'temp_poly']].round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b1MbGxhZbXlQ8OnpeFbPZXOod8Zo2bVpsW3a7ndWrV/O///2PSy65hISEBIKDg5kyZQpAiTtm1apV6z/395ZbbqFv37689957dO/enYiICHr16sW0adO8kymnpqaSlZVF8+bNvXPsHKnoC2TRsStSs2bNYs+LvpAfaw6dk93W6ZKWlgaUjP1kFM1/NXHiRKKjo4s9mjVrBsDOnTuLrXN0zjZv3ozH4+Hzzz8v0Ud0dDSbN28u0UdUVFSJotrxHPuTUV7n/qmyWCykp6czefJkLr/8clq0aEFQUBCXXnopUPK981+O9TlxvO8HKMzLiW67Z8+ehIeHe+fCys3N5fPPP2fIkCGlti86D2fMmFHi/ImJiSE3N7fEOVSWo/MdGBgI4J2X6ljHpW7duoSHh3vbFN1MoLTC+dFFqQcffJBzzz2Xl19+mU6dOhEVFcWAAQN4++23T+jzXUREqg6rrwMQERH5L507d2bbtm2sWLHC+z/6pbn22mspKCjg2Wef9S4rrSAC/46uOHJ0RNEX8aOV1cfRLBZLqSNzirZV1P8111zDnDlzaNiwIV27dqVnz5506NCBNWvWcOutt5ZYv6y4juTv78/ChQtZt24dCxcuZMmSJSxdupRvv/2Wp556qtgE8CdyTIr262Sd6LZOl+XLlwPQvn37U+6rqAAzefJk70ilo0VGRhZ7fnTOivoYNGgQN954Y6l9HH2sTscor+NVXud+WY63oPP6668zduxYwsPDueiiixg6dCht2rQhNjaWjh07HlcfpSltP8/E++FINpuNgQMHMm/ePLKzs/niiy/Izc0t8657RefQLbfcwoABA0ptU1Rc+i9m8/H9v/Sx3r9Fx6WoTWk5PnI0KEB0dDS//fYby5Yt48svv2TJkiV8++23fPbZZzz//PP8/vvv+Pv7H1dsIiJSNagoJSIiFd4111zD7NmzmTZtWplFqd27dzN79myio6OJioqiQYMGQOHd947m8XhYu3Yt4eHhJYoJp8LpdLJr1y7vtots2rSJqKgooqKi+P3335kzZw4XX3wxX331VbEvuF9++eVJb3vbtm0cPHiQ8847jxYtWvB///d/OBwO7rrrLqZNm8b777/PLbfcQnBwMOvXry+1j9WrVwP/XqJ1KoruAFge2zqaYRi8++67WCyWEpe/nYyGDRsChZfTXXTRRcVey83NZeHChcTFxR2zj6L9zM/PL9EHFF7+V6NGjVOO1VeO59yHwuLp0Xe+BNi/f/9/bsPtdnPnnXcSFRXFhg0bvH0Cx303yKMd+TnRuXPnYq+dyXO0yJVXXslbb73FwoUL+fDDDznvvPPKPJeKzkOTyVTiHDIMgw8++OC0nUNHHpe2bdsWe23Hjh1kZWV5j0uTJk34+uuvWbduXYnC4JYtW4o9X79+PXl5eZx99tmcffbZAGRlZTFq1Cg++eQTvv32W/r163da9kFERCoHXb4nIiIVXo8ePbjkkkt49913eemll0q8np2dzdChQ3G73TzyyCNYrVbat29PvXr1eOedd9i8eXOx9nPnzmXnzp0MGDDguEeCHK8jR2lB4Zw1mzZtYujQoZhMJg4ePAgUfuk7siCVlJTkvYX6iV4GBHD77bfTvXv3YnP92O1270ihopEs/fv3Z8uWLcyePbvY+lu3buW9994jISGBNm3anPD2j1ae2zraY489xl9//cX1119P7dq1T7m/nj17EhoayvPPP1+ioPLUU08xZMgQfv7552P2ERMTwwUXXMCXX37J77//Xuy1H3/8kf79+zN9+vSTiq9ohMrJnDen03+d+1B42VhKSop37iIoLNS9//77Jfo7er+ysrLIyckhMjKyWDHZ7Xbz9NNPF2tb2vqlKSpaPvzww8XaeTwe79xJp6OwWZaLLrqIqKgo5s6dy9dff13mpXsA7dq1IyEhgVmzZnkvmSvy9ttvM2TIED766CPvslM5L/r374/ZbOaJJ54gNze32GtFlxkXHZerr74agCeffBLDMLztfvnlF3799ddi6w4ePJi+fft6LykGCAkJ8V7mV56jA0VEpGLQSCkREakU5s6dy8UXX8ztt9/OBx98wIABAwgNDWXbtm28/fbbJCUlcccdd3gn8LVYLLzxxhtcdtlldOzYkZtuuonGjRuzcuVKXnvtNRISErxfOk+nN954g6SkJC688EI2bdrEjBkzSExMZPLkyUDhpYiRkZHMnDmTwMBAWrRowdatW3nzzTeJjY3l4MGDZGRknPB2J0yYwKJFi+jcuTPXXXcdsbGxbN68mRkzZlC3bl3vl90nn3ySpUuXMnr0aH788Uc6depEUlIS06dPx2Kx8Oabbx73pT3/5Uxv69NPP/WODCkoKCA1NZUvvviCJUuW0LVrV5555pnTsh9hYWG8/PLLjBo1ipYtW3qLXUuWLGHu3Ln06NGDa6655j/7mTZtGl26dKF79+5cf/31tGzZki1btvDKK69Qt25dHn300ZOKr2j+qkceeYQLLrig1JFY5eG/zn2AIUOG8MMPP3DRRRcxduxY8vPzefPNNwkICCjRX9F+Pffcc/Ts2ZMBAwZwwQUXsGTJEi6//HIuvfRSUlNTeeedd/B4PPj5+RV77xStP2/ePIASE+5DYVFozJgxvPnmm3Tq1IkhQ4YQEBDABx98wE8//cRNN91E165dT+dhKsZqtXL55Zfz+uuvY7FYGDx4cJltLRYLr732Gn369KFdu3aMHTuWhg0b8tdff/Hmm2/SqlUr7rrrLm/7UzkvEhMTmTRpEg8++CBnnXUWI0eOpEaNGnz55Zd89tln9O3bl+HDhwNwzjnncPvtt/Piiy/Ss2dPrrjiCnbv3s2LL75I7dq1i42Ce+CBB7j66qvp1KkTI0eOJCIiwvuZ3LZtWy6++OITPYQiIlLZ+fjufyIiIsetoKDAmDZtmtGtWzejRo0ahtVqNWrUqGH069fP+Pbbb0tdZ926dcbQoUONmJgYw8/Pz0hISDAmTJhQ7JbuhmEYgNG1a9cS6x/v8qLb13/66adGly5dDLvdbtSsWdMYO3askZqaWmzdP//80+jVq5cRERFhBAYGGp07dzbeeecd49ChQ4bVajX69u37n9t/6KGHDMD48ccfvct+/vln47LLLjNq165t2O12IyEhwbj11luNAwcOFFs3LS3NuOuuu4wGDRoYfn5+RkxMjDFixAhj06ZNxdoV7dOOHTuKLd+xY4cBGCNHjiwR19GOd1tvvfVWsVvSH0vRbe2PfFitViMmJsbo2bOnMXPmTMPlcpVY7+hjWdY2yzrmP/zwg3HJJZcY4eHhhr+/v9GsWTPj0UcfNXJycrxtfvzxRwMwHnrooVJj37p1qzFy5EgjNjbWsNlsRv369Y0bbrjB2LNnT7F29evXN+rXr19i/dJiXrVqldGyZUvDZrMZvXr1KnW7R/Z79J9/5XnuG4ZhzJgxw2jevLlht9uNevXqGQ899JDxxx9/lNivPXv2GOeee65ht9uNZs2aGYZhGElJScbo0aO9x6958+beHHTo0MGoUaOGkZeXZxiGYTidTmPYsGFGUFCQERwcbGzdurXMnM+ZM8fo1KmTERgY6H1Pvvfee8XaHCu3Refkfyktr4sWLTIAo0ePHsWWlxXrX3/9ZVxxxRVGjRo1DLvdbjRq1Mi4++67jbS0tGLtSjsvyoqzrH374osvjB49ehghISGG3W432rVrZ8yYMcNwu93F2nk8HuPFF180EhMTDZvNZiQkJBgzZswwxowZU2J7CxcuNC688EIjJibGCAgIMBITE4377ruvxGeyiIhUDybDOGKcrYiIiJyUUaNG8fbbb7Njxw7i4+N9HY5IudG5LyIiIidLc0qJiIiIiIiIiEi5U1FKRERERERERETKnYpSIiIiIiIiIiJS7jSnlIiIiIiIiIiIlDuNlBIRERERERERkXKnopSIiIiIiIiIiJQ7q68DON08Hg/79+8nJCQEk8nk63BERERERERERKoVwzDIysqidu3amM1lj4eqckWp/fv3ExcX5+swRERERERERESqtT179lC3bt0yX69yRamQkBCgcMdDQ0N9HM2p8Xg8pKSkEB0dfczKolRsymPVoDxWDcpj1aA8Vg3KY+WnHFYNymPVoDxWDVUpj5mZmcTFxXlrNGWpckWpokv2QkNDq0RRKj8/n9DQ0Ep/QlZnymPVoDxWDcpj1aA8Vg3KY+WnHFYNymPVoDxWDVUxj/81rVLV2EsREREREREREalUVJQSEREREREREZFyp6KUiIiIiIiIiIiUuyo3p9TxcrvdOJ1OX4dxTB6PB6fTSX5+fpW5nrQ6qsh59PPzw2Kx+DoMERERERERqYaqXVHKMAySkpJIT0/3dSj/yTAMPB4PWVlZ/zk5mFRcFT2P4eHh1KpVq0LGJiIiIiIiIlVXtStKFRWkYmJiCAwMrNBfxA3DwOVyYbVaK3SccmwVNY+GYZCbm0tycjIAsbGxPo5IREREREREqpNqVZRyu93eglRUVJSvw/lPFbWYISemIucxICAAgOTkZGJiYnQpn4iIiIiIiJSbijXBzRlWNIdUYGCgjyMRqTiK3g8VfY41ERERERERqVqqVVGqSEUbrSLiS3o/iIiIiIiIiC9Uq8v3Tpf8/Hw+/PBDPv30U9LS0oiKimLAgAEMHjwYf39/X4cnIiIiIiIiIlLhqSh1ghYsWMCoUaM4fPgwZrMZj8eD2Wzmk08+Ydy4cbz99ttcdtllvg5TRERERERERKRCq5aX752sBQsWMGDAANLT0wHweDzF/k1PT6d///4sWLDgjGx/5cqVDB48mJiYGAICAoiPj+f2228nJSWlzHV69+7NY489dlz9BwcH8/PPP59ynJMmTaJbt26n3I+IiIiIiIiIVF0qSh2n/Px8Ro0aBRTeTa00RctHjRpFfn7+ad3+V199RefOnWncuDF///03OTk5fPXVV+zatYv27duzd+/eMte79957j2sb2dnZdOnS5XSGLSIiIiIiIiJSKhWljtOHH37I4cOHyyxIFTEMg8OHD/PRRx+dtm07HA7GjBnD2LFjeeyxx6hTpw5ms5lmzZrxySefUKtWLcaNG8esWbPo0KEDAwcOJCwsjHnz5tGtWzcmTZoEQF5eHtdffz3h4eHExcXxyCOP0LBhQxYvXgwUTnhd9HN8fDxPP/007du3Jzg4mLPPPptly5Z5Y5o3bx5t2rQhPDyc8PBw+vfvf8wRWyIiIiIiIiIiR1JRCujQoQN169Y95uOGG244oT6vv/76Y/bXoUOH4+7rt99+48CBA4wZM6bEaxaLhdGjR/P555/jcDj4888/GTx4MMnJyfTv379Y24kTJ/L333+zdu1a1q9fz+bNm9m+fXuZ2501axZz584lPT2dVq1aMXLkSAD++usvbrjhBmbOnEl6ejpr165l48aNvPjii8e9TyIiIiIiInJq8vPzmTNnDn1u6EPv6b3pc0Mf5syZc9qv3BE5UzTROZCUlMS+fftOa5/5+fmnrc/9+/cDhaOXSlO/fn0KCgpIS0sjJCSEoUOHYjKZirXxeDy8/vrrfPzxx9StWxeA6dOnM2/evDK3O2bMGJo2bQrAoEGDmDVrFgDNmjVj1apVJCQkkJ6ezv79+4mJifHGKSIiIiIiImfW3I8XcPv4e8guyKT28BDsURY25G3nunsmcMf9j/LS809z9eW6CVdFl5btINvhAgq/t6elO8iz5GA2F44hCrZbiQq2+zLEM8onRanly5dz9913s2rVKgICAhg4cCBPPfUUQUFBTJ8+neeff56DBw/SoEEDJk+eTL9+/c5oPLVq1frPNmlpaSdUbfb39ycqKuqUtlkkOjoagH379tG4ceMSr+/evRs/Pz+ioqKIjIwsUZACSE1NJTc3t1hhKzQ01Nt3aSIjI70/BwQEeCd0N5lMvPzyy8yfPx+LxULr1q3JzMzE7XYf9z6JiIiIiIjIyZn78QLGf7Aae/ebCKv/N6G1NlLH7WBfgh3zDZ3I39WOO99fhWEYDLvizH6flpOXlu1g3Ht/czjXCUAD53b65C1gVkA/dvglABAR6McLQ86qsoWpcr98LzU1lV69ejFo0CBSUlJYtmwZy5YtY8KECXz++efcd999zJs3j8zMTN58802GDRvGxo0bz2hMK1asYO/evcd8vPbaayfU5+uvv37M/lasWHHcfXXp0oWaNWvy6quvlnjN4/Hw5ptvcumll2K3l32S1qhRg4CAAHbt2uVdlpubS2pq6gntF8Bzzz3HDz/8wIoVK9i6dSuffPLJMYtbIiIiIiIicnrk5+dz+/h7sASEYgnZiy1qG9FuB7GmXKLdDmxR2zEH78ESEMrt4+/RpXwVWLbDxeFcJ3aLmVC7hT7u7+ngWUkf9/eE2i3YLWYO5zq9I6mqonIvSu3YsYOuXbty6623YrVaqVu3LiNGjGDJkiVceuml7Nixgw4dOhQOW0tLIy8vj/Xr15d3mCUMHjyYiIiIUkchHclkMhEREcGgQYNO27b9/f156623mD59Ovfddx/79+/H4/GwYcMGBg4cyIEDB3jhhReO2YfZbOb666/nwQcfZN++feTk5DBu3LiTGt2UlZXl7bOgoIDXX3+dH3/8EafTeVL7JyIiIiIiIsfnww8/JDMzAywO/OPW4G9xEIsDp2GhFg78LfkE1FsDFgeZmRmn9SZccmbY/cw0ZSfNnevINQfT3LmOpuzE7lf1pwEv98v3OnbsyPz5873PDcNg4cKFdOzYEbPZTHh4OJs2baJly5a4XC46depEr169yuzP4XDgcDi8zzMzM4HCEURFl5sV8Xg8GIbhfZwIu93OrFmzGDBgACaTqdT1iwpWs2bNwm63n/A2SlPUxyWXXMLSpUt59NFHadOmDVlZWdSsWZO+ffvy2muvERMT42179HaL9vfJJ59kwoQJdOjQAavVyo033ojJZMLPz6/YumX9XPTvXXfdxdq1a6lfvz5BQUFcccUVPPbYY8yePbvUdYQKfUyKclbae0b+VfT5oWNUuSmPVYPyWDUoj5Wfclg1KI+Vz/z58zGZzfjXXYc1KJM67gJcfh5sHjP+Jjf13U42h2QTEL+N5h06kfrHu3gaZXvXX7c/kxxH4eCEpIj2pIcUThETmH+QhLTFtKsX7m274UAWWfmFo3SSw9tyKLRwzuEARyoNkhfRIT7C23bzwSzScwvbpoS1JC2sJQD2gsM0SPqGsxv823ZrcjaHcgoHNaSFNiUlvC0Afs4sGh74gvb1w7GYC79fb0/JITW7AIDDIY05GNEeAIs7j8b7PqNdvXCslsK2O9NySM4sbJsR1IADUecAYPY4abL3Y9rEhWG3FhZ6dh/KIymjcBRZVmAc+2p0LgzOMGi6531a1QklwGYBYN/hPPalF7bNDqjN3ugLvPuSuOcDWsYGEWQvLK8cyMhnz6E8AHL9Y9gdc6G3beO9n9Cipp1Q/8K2eQez6J6RhF8WnGv8hdmTR4o1llhPEudlf8OG4BsxjNLrGxXd8cbr04nOnU4nY8eOZcOGDcyePdu7vGHDhuTl5bF69WqmTp1KSkoKQUFBpfbx+OOP8/DDD5dYnpKSUmKYotPpxOPx4HK5cLlOfPhb7969+eijjxgzZgzp6emYzWY8Ho/337CwMGbOnEnv3r1Pqv+jGYbhHclkMplo1aoV7733XqltXS4Xw4cPZ/jw4cW2/d1333lf/+2337j//vt55plngMJ5sh544AEiIyNxuVwUFBR4227ZssX7M8D5559PQUEBLpeLsLAwPvnkkxIxjB8/HpfLxf3338/9999/Wo5BVXB0Hisal8vlHZno5+fn63AqLI/HQ0ZGBoZheCcdlMpHeawalMeqQXms/JTDqkF5rHySkpKwhIF/1C5CrLnk+BeQg5mGDjdmIMriIIAQiNxFdEId2gT8RcGGr73rW9PysDkLCwabk/1Z5R8IQJxzB01zfqAgJ/DftofysBUUtt0WaGGFfwgAsa69JGb9QEHev9/TzYfzsf1T7NoZ4OH3gMIiVA3XQRIzf6Ag/4i26fnY8gvb7vXPZ0lg4XQwYe5DNMv4AVduIO5/ilLmDAe2vMLvlvvt2fwQVDg/c6AnmxbpP+DMDcTzT1tTpgPbP4Wxg/a2fHeg8CZffoaDlod/wJUTgMlSeJ6bsgqw/VMYS7U157uk+MLgDIPWh3/AlR1AwT8FLLILsGUXtk33a8x3Bxt696X54cW4Mq0U+BUWsMgpwJZV2DbJGs93yYnetk0O/4Qn3aDgn2JXQGYBHVx5hJBLvGk/u6mDYUC6KZRGeWuoa9nIanc8aWmHCHDnlDgXKrKiK6z+i8+KUikpKVx55ZUkJSWxZMkSYmNj/w3KWhhWu3btaNWqFbfffjsLFiwotZ+JEycyfvx47/PMzEzi4uKIjo4mNDS0WNv8/HyysrKwWq3ebZyogQMHeotTn376KWlpaURFRTFgwAAGDRqEv7//SfV7LKerUPDCCy8QEhLCa6+9hslk4qmnniIxMdF7hz05sypqwcdqtWI2m4mKijoj529V4fF4MJlMREdH6w+2Skx5rBqUx6pBeaz8lMOqQXmsXNxuNx6Tmwt7ROOwpZHu58ZtgM2AHKzYDAAPQZZMMlKasmfdbn5uWZeuHYZ7+zAOZOEpKCwItQ9vTtPg+gDYHKF4DhVgqxv27waTsvH8M59R67CmNAxpAIBfQQTulKuwHTGqypScjeef4lHL0EbUDy1sa3XWwHPwKmz1j2ibkoPnn8m9E0MaEBtW2NbiisGTdBV+cWGYiwpNqbl4cgoHUDQKrkd0eGFbs9uB58BV+NUJxfpP8ch8KBdPVmHb+KA6XBVR2NbkceLZfxV+dUKwWQsLQqZDeXiyCq+6qhtQi6uiGvxzgAw8+67CGhuC7Z/ikSk9D09GYdta/tFcVaPBv/uybzB+NQOx/TNSypSRj+efUVXR9kiuiv63rXX/FViibdj8C78b5iZl8cMfu+lrLMbj8cNlCcZiBocphHBXJj0KFrPedh1RUZHERJU+UKeiOt7vlj4pSq1evZo+ffrQsWNHPvvsM2/x6LnnnuOPP/7g/fff97bNy8tj7969ZfZlt9tLneDbbDaX+FA1m82YTCbv42QFBAQwYsQIRowYcdJ9HA/DMLxxno4RNi+99BI333wzderUwePxcP755/P5559XyNE7VcnpzuPpVvR+KO09I8XpOFUNymPVoDxWDcpj5accVg3KY8Xn8Xh4/8OPmfTah2RFn0tC8yV43AZ4DNyGiag8Mx4gD8iwQ4KzAPuBYH5at4qEsU9jbvXvnMfNW5W1lTpA62JLEo/ZtkWxJSXvE3+k4gMhGpbRqlCTYs8a/PMoXUKxZ/X/eZSq3XXFntb751Gqs4q3rfvPo1Rtry32tPY/j9Lbjiz2NLB2DulrPiA6N4NDttr4mS2Fl72ZTGRaImhasJZG1m2Yze0r3fvzeOMt973av38/PXr0YMiQIXz88cfFRjNdcMEFfPrpp3z66ad4PB6WLl3KCy+8wFVXXVXeYVZJcXFxLFy4kEOHDpGens7nn39Oo0aNfB2WiIiIiIiIlMIwDN7/4D0G39SV2+b8yuGIZris4aRti+O8lABuP5BPgNtMvhUsJnBYwWqYufJwPkP81xEaGnpab8Ilp5lh0MPxPQGeHJyGGYs7D6s7D4s7D6dhJsCTQw/H91AB5yY+Xcp9pNT06dNJTU1lxowZzJgxw7u8fv36rFu3jg8++IAHH3yQESNGULt2bR544AHGjRtX3mGKiIiIiIiI+IRhGHz66Ue8/8NUzPHZWFp4OMu2n7+3eahvSqVD9Dk0yPiU+nYb52a5WRpmIhDIt0KXDIN6uTY8pkO8/NwTmqKjAgv2g1rmw2SbAvF3F87BZBgGJqPwCptsUyC1zIcJrpgzwZwW5V6UmjJlClOmTCnz9f79+9O/f/9yjEhERERERETE9wzD4IsvPuO9T+7FFZ+Dp1EwHpMZa4GZMEcys28bTe9LemEymfjo/XhGT36IbGc21itC2R9rxdjj4p1PMvnUL5h7H7yToVcM9PUuyTFEhQVjGv06edmHgcLLNNMPZxAeEea9/K1xcASRYcG+DPOM8und90RERERERESqO8Mw+Oqrr5n95eMQsxt3MzNgwZKdDwdiueri8fS7a2CxOWoHXTWMvv2v4KOPPmLu4rkkWZOolVKfYQ8MO2M34ZLTLzKmNsQUzkLl8XjwS04mJiam0s0hdbJUlBIRERERERHxAcMw+P6HH/i/52aywxNB7255FNj8MWfnYdkRwODLHmHAvYPLvGGSv78/w4cP5+qrrya5mhUzpGpQUUpERERERESknP3w4/e8/tEUlu5uhCOgERZg++YE4gJ2MajrQ1xx31UqMEmVp6LUCUrLdpDtcJX5erDdSlSwvRwjEhERERERkcril6VLmf7egxCXhjvRQ8cQf35eH05Ezi5uGTSIoVcOUjFKqg0VpU5AWraDce/9zeFcZ5ltIgL9eGHIWae9MBUfH09SUhJWa/GUnXfeeXz77bendVun26xZs5g0aRI7d+4s8drOnTtp0KABgYGBJYakTpgwgQceeIDg4GC++uorunTpgslk4scff6Rbt27FfhYREREREanIfv/tN16bOY7cWgfwNAwGixWL24QnK4NnrjyL4UOfwGKx+DpMkXKlotQJyHa4OJzrxG4xY/crWbl2OD0cznWS7XCdkdFSr7zyCqNGjTrt/VYE69atIz4+vtTXsrOzyzcYERERERGR02TFihVMnXU/rshNuJuYAD/M+Q44EEr3tqO49oXrSww+EKkuNCbwH/lOd5mPApenWFs/qxm7n6XEw89qxjD+u98zEn9+Pvfddx8NGjQgIiKC7t27s3z5cu/rJpOJCRMmEBMTQ/fu3bHb7fzyyy8AOJ1OQkNDGTdunLf9iBEjuOeeewB48cUXSUxMJCwsjKioKEaOHElubi4Ao0aNYtiwYbRp04YaNWqwfft2tm7dSs+ePYmIiKBt27asWLHilPbNZDKxePHiU+pDRERERESkPP39999cfNV19Py/13DXyMUTGYDZaWDe6OHi6Dt459ml3HDtTSpISbWms/8ft8z9q8zXWtUN446Lmnifb0nOLrWa5/YYWCxHXYL28Wqy84vPQfXmqI6nFGtp7rnnHn7++WcWLVpEXFwcL7/8Mj169GD16tXeEUjr1q1j79695ObmcvXVV/PVV1/RuXNnfv31VxwOB4sWLSrcD7ebL7/8ki+++IL58+fz1FNP8dNPP9GwYUM2bNhAly5dmDdvHmPGjAFgwYIFrFixgpo1axIcHEyrVq24+OKLWbBgAbt27eKSSy457fsrIiIiIiJSEa1auZLn3ryfZXuCSLM2xRoOa7aYaBHzF92bXMu1M27HZrP5OkyRCkEjpSqRm2++mfDw8GKPnJwcDMPgjTfe4NFHH6Vhw4bYbDbGjx9PYmIi8+bN864/atQobDYb4eHhDBw4kK+//hqAb775htGjR7Np0yYOHDjA0qVLCQgI4JxzzuGiiy7it99+o2HDhqSkpJCamkpUVBT79+/39tu1a1cSExMJDw/njz/+YOvWrTz++OP4+/uTmJjI+PHj/3PfWrduXWy/unfvfvoPoIiIiIiIyBmybu1aRt/Wj6c+H0Ve412cddZ+DLcTe/I6xnRqy+wnljL2pv+pICVyBI2U+se0Ye3KfM181ATcjWOCCbCVnIAur8BN1lGjop68ovXpCRCYPn16qXNKJScnk5eXR0JCQrHlCQkJxSYXr1Gjhvfn/v37c/PNN5OSksI333zDs88+y99//82iRYtYuXIlAwcOxGQyUVBQwMMPP8zXX39NcHAwZ511Fg6HA7fbXWq/+/btIzIykqCgIO+yxo0b/+e+HTmiS0REREREpLLYtGkTL7x0O4cC1+JpEAxWG2bDhDsjn3u61uK2sQ/i7+/v6zBFKiQVpf7h73f8dzkwm8ByVKGqaPnRi0+k35MVHR1NYGAg27Zto2nTpt7lW7dupV+/fqWuExMTw7nnnss777zDli1bOO+88+jVqxfff/89v/76K6+++ioAEydOZO/evWzcuJHg4GAAGjVqVGYs9erVIyUlhczMTEJDQwHYu3fv6dpVERERERGRCmHr1q088fJEcgL+wlXfAOyYnE7Me4NoW6c/4x69h4CAAF+HKVKhqSh1EhxOD+AqY3n5M5lMXHvttdx7770kJiZSr149Xn/9ddavX8+7775b5noDBw7kscce44ILLsBms9GrVy969+6N3W7nggsuACArKwuTyYTFYiEnJ4dnn32Wbdu24XQ6S+3z7LPP5qyzzmLChAk8//zz7Nu3j6effvqM7LeIiIiIiEh527FjBxMfn8q3mw7T9dwc/GoFYMrPgW1OOja+hpsn31fsyhERKZvmlDoBwXYrEYF+ONweMvNdJR4Ot4eIQD+C7eVf63v66ae57LLLuOiii4iJieHjjz9m8eLFJCYmlrnOwIEDSU1NpWfPngCce+65WCwW+vXrh8VSOMLr0UcfJTc3l+joaJo1a4bT6eTOO+9k5cqVpfZpNptZsGAB+/fvp3bt2vTq1YuBAwee9v0VEREREREpT7t37+amu65m8D238sOhcKzRDfhzewvY7U9rz5W8+ewG7r77MRWkRE6AyTAMw9dBnE6ZmZmEhYWRkZHhvXysSH5+Pjt27KBBgwYnfU1vWraDbEfJUVJFgu1WooLtJ9X30QzDwOVyYbVaMZVyuaBUDhU9j6fjfVEdeDwekpOTiYmJwWxWPb+yUh6rBuWxalAeKz/lsGpQHv/b3r17eXzqRA4HrcUV5cKWZ+fbZb3g4Bau63kW/3fHLSW+e5Y35bFqqEp5PFZt5ki6fO8ERQXbT1vRSURERERERCqmAwcO8Nzzd7Hb8ROeeoGY/PwxAe7DHka1NHPvm68RHh7u6zBFKjUVpURERERERET+cfDgQR5//n6SXD/hrukC7OBxY94L9f27MuGeR4iMjPR1mCJVgopSIiIiIiIiUu2lpKTw8NMv8t6vW2jb1iCyiR0cLsy7HDQKv4Q773mSGjVq+DpMkSpFRSkRERERERGpttLS0nhy6kP8sW0DGxydMddKZHWShwtsO6hv78g9DzxOzZo1fR2mSJWkopSIiIiIiIhUO4cPH+bpFx5iR/5SPLWd1Im0sP33AnL37+HyDnE8cvdnxMbG+jpMkSpNRSkRERERERGpNjIyMnjxxUms3f8BnoQATJEBhS+km7i09mEefO4p6tSp49MYRaoLFaVERERERESkysvKyuLZFx9nQ+pneGLzIMEOeLAkOYkoaM//3f449erV83WYItWKilIiIiIiIiJSZeXk5PDsS68w/fPfqd0onEbtzFBgxnwgj1ruDtx5xwvEx8f7OkyRaklFKREREREREalycnNzmTrtKX5a9z1/Z1wAtZqzNxvqbQmjpqUx99z2NA0bNvR1mCLVmopS4lMHDhwgNDSUoKAgX4dyTG63m927d9OgQQNfhyIiIiIiIseQn5/PyzOeYfneTzHVKyCiHcSsyGTf7my6xQfy5G0zadKkia/DFBHA7OsAKrUDq+Gz2wr/PcNMJhOLFy8u9bXHHnuM3r17n/EYjsekSZPo1q3bcbU9ePAgjRs3JiUl5cwGVYZu3boxadKk42o7ZMgQ3n77bQB2795NcHAwu3fvPoPRiYiIiIjIiXA4HLz80uOMvLUJyxwzMeo78JgMLJlm2gYd5JfpdzN/1jQVpEQqEI2UOlmGASvfgW3fgy0Aaj0JJpNPQrn33nt9st1TlZeXR05Ojq/DOC5HFs7q1atHdna2D6MREREREZEiBQUFzHhjGr9unIMRexgjsfBrrjU1H//0ptw5Zgqt27TxcZQiUhqNlCrizC/74Soo2XbPctj5C9hDC//ds7zstkc/TrMjRyfNmjWLLl26MH78eKKjo6lZsyZ33XUXbrcbKPzAvvfee0lISCAkJISLL76Y9evXe/v67rvv6NSpE5GRkYSFhdG9e3e2bt3q7btDhw4MHDiQsLAw5s2b95+xmUwmZsyYQdOmTQkNDeXCCy9ky5YtuN1uWrRoAUCLFi34+OOPAXjnnXdo1aoVISEhtG7dms8++8zbV3x8PHfccQd169albdu27Nixw9t/bGwsMTExXH/99cUKRnPmzKFVq1aEhobSqlUr5s6dW2qcqampDB8+nFq1ahEcHEyjRo146623ALjuuuv4+eefeeyxxxg4cCA7d+7EZDKxc+dOADZu3Ejfvn2pUaMGdevW5ZZbbiEjIwOAxYsX06BBA55++mnq1q1LZGQko0ePVlFLREREROQUOZ1OZrw+kyYXD+P5xTswJ7gxbH5YDhcQsa0uEwZ+wFsvLlRBSqQCU1GqyIcjy34sfa5424+vg0/GwKHtkJNS+O8nY+CDa2DxY8XbLri1ZH9n2C+//ELt2rXZtWsXP/zwAzNmzOCjjz4CYOLEiSxcuJBvvvmGw4cP079/fy6++GJyc3NJSkqiX79+PPjggxw6dIgdO3bgcrl4+OGHvX3/+eefDB48mOTkZPr3739c8bz//vt89913HDx4EJvNxu23347FYmHdunUArFu3jiuuuIKFCxdy0003MXXqVDIyMnj55Ze59tprWbFihbevn3/+mQ0bNrB48WJM/4xM++ijj1izZg2rV69m1apVjB8/HoD58+dz22238dJLL3H48GGef/55xo4dyyeffFIixjFjxmCz2dixYweZmZncfPPN3HjjjeTl5fHGG2/QpUsX7r33XubPn19svczMTC6++GJatGjBnj17WL58OevXr2fEiBHeNrt37+bgwYOsW7eONWvW8M033zBt2rTjOnYiIiIiIlKcy+Xi1TenM/iW7jz4+QZyo5uT64kgfXMMAdvr87+B83ntlSW0PessX4cqIv9BRamTkZ8BeYfBYiu8ZM9iK3zuyPR1ZACEhIRw1113ERgYSIsWLWjVqhWbN2/GMAxeffVVHn/8cRo3bozVauXWW28lNDSUL774gsjISFavXk3v3r3Jyspi165dxMTEsH///mJ9Dx06FLvdTmBg4HHFc/vttxMXF0dAQAD9+/dn8+bNpbabMWMGN954Iz169MBsNnPBBRdw5ZVXekcsAQwdOpSQkBDCw8O9y6ZOnUqNGjWoVasWjzzyCLNnz8bj8fDqq69yww030K1bNywWCxdddBFjx47l1VdfLbHtl19+malTp+Ln58euXbsICQnB6XSSmpp6zH378ssvcblcPP744wQEBBAbG8vLL7/MwoULOXDgAACGYXDvvfcSFhZGnTp16NKlS5nHQERERERESud2u5n19msMG9+ZxZmvYGuZQbPYJNyZKTQzdjNp5BRmT/uODh07+jpUETlOmlOqyOC3y37NdETtzjCgRhPI3AcRDQqLUoYBh3dATAvoOrH4uv1ePjPxHkNERIR3FBFAQEAAbreblJQUcnJyuPrqqzGb/92ngoIC9u7di5+fH++//z6zZ8/G4XDQsmVLMjIysFr/PU0iIyOL9X08IiMjS8RSmp07d7J48WLeeOMN7zKXy8XFF1/sfV6jRo0S6zVr1sz7c3x8PA6Hg7S0NHbv3s2AAQOKtU1ISGDBggUl+ti+fTsPPPAAmzdvJj4+nsTERIAyYy2ye/du6tevX+x4JiQkePenyPEeAxERERERKc7j8fD+e7NZ8N0UPA1dGA2DcJvM+OWZqeVJ5bGHxnJ+586+DlNEToKKUkX8/I+v3YFVsHcZBMeA5YjDFxxTuDxlA9Rue+L9loMaNWrg7+/PN998Q6dOnbzLt2/fTs2aNZk3bx7Tp0/njz/+IC4uDiicT6loTqkzLS4ujhEjRjBx4r+FvQMHDuDn53fM9fbt20f9+vUB2LFjB0FBQdSoUYP69euzbdu2Ym23bNlC7dq1iy1zOBz07duXqVOncu2112IymVi6dCmzZ8/+z5jr16/Prl27cLvdWCwW7zYAateuzY4dO/57x0VEREREpASPx8P7H7zPp0tfhNgDeJqbATPWjHw4WI8xg+7nwgk9fB2miJwCXb53IgwDVr1beJme2QoFuf8+zNbC5aveLWx3BiQlJbFz585iD6fTedzrm81mxowZw4QJE9i9ezeGYfDJJ5/QvHlztm3bRlZWlred2+3ms88+49133z2hbZwIu90OQHp6OgDXX389L774In/88QeGYbBmzRrOOeccPvjgg2P2c88995CVlcX+/ft58MEHuemmmzCZTFx33XW8+uqr/PDDD7jdbn766SfefPNNRo0aVWx9l8tFfn4+ZrMZk8nEtm3bmDBhAoB33+12uzfOI/Xq1Qur1cqECRPIy8sjOTmZu+66i27dunkLZSIiIiIicvwMw+CjTz6lZa+hjHvnD/ziCzAC7VhyXARsDOG6zjOYN+1HLuyugpRIZaeRUifC44LMA4V33MvPKPm6PbTwdY8LLMce3XMyhg4dWmLZhg0bTqiPZ555hocffpiuXbuSlpZGw4YN+fDDD2ndujWJiYn88ssvtGjRAqvVSu/evZk2bRp33XUXBQUF/935CapVqxa9e/emc+fOTJ06leuvv56cnByuu+46du3aRVRUFLfccgs333zzMfupX78+zZo1w+PxMGzYMKZMmQLAFVdcQW5uLuPGjWPXrl3Ex8czbdo0hg8fXmz9oKAgZs6cyQMPPMBtt91GixYtmDRpEmPGjGHlypU0btyYIUOGcMcdd7By5UpmzZrlXTc8PJzvvvuOe+65h7i4OOx2O5dffjmTJ08+7cdLRERERKQqMwyDTz/7hHnfTuXnPW1whzUFYO/GetSypzK63//R86FLT3g6ERGpuEyGcYaG9fhIZmYmYWFhZGRkEBoaWuy1/Px8duzYQYMGDfD3P8nL6nLSwFFKQaqIPQyCok6u76MYhoHL5cJqteqDtxQ7d+6kQYMG7Nixg/j4eF+HU6aKnsfT8r6oBjweD8nJycTExBSbQ0wqF+WxalAeqwblsfJTDquGipBHwzD46quFzP7iKcwNsnDZPOTtbsBvGxKo4zrIo+NG0Kd37wr5t3RFURHyKKeuKuXxWLWZI2mk1IkKijptRScREREREZHqyjAMvvvua+a8dw/O+pl4GgfhMVuwOs3YstJ544Ye9O/XV8UokSpMRSkREREREREpV99//z1vfjIFT8xO3M1MgAVLTj7GgVr07zqOK8dfqWKUSDWgopRUWvHx8VSxq09FRERERKq0n5Ys4f+efYPN+aFc0sWBJ9gfc3Yepm1+XH7JgwyaOKzSX7YkIsdPRSkRERERERE5o35ZupTp703i9711yPJrgMUPNm5uTKOwjfQ/9x6umngNFovF12GKSDlTUUpERERERETOiD9+/52X5tyPEZeCs4mHDhEeflhVk9DMHdx6bX+uGTZTxSiRakxFKRERERERETmt/lyxgumvjSOnxi48jYLB4ofFbcKVnsOjA5pz7TWPYbXq66hIdadPARERERERETktVq5cyXNv3I8zfD2uxgA2TA4HpqQQOjcdzo3P3Yyfn5+vwxSRCkJFKRERERERETkla9asYcITL/P7ATc9OjkwRQdgys3FtM1Dt3a3Mmb8OGw2m6/DFJEKRkWpU/DXwb+Yu2Euw5oNo13Ndr4OR0REREREpFytX7eOZ167j7/2wX7aYI2EP7cG0jE3nfPih3Hjy+Px9/f3dZgiUkGpKHWSsgqymLl2JhsObaDAU0DjiMaE2EJ8HZYcp61bt9KoUSNfh/GftmzZQuPGjX0dhoiIiIhIMVu2bOHJaRPIi9yOs6GbFjWD2Le8ObbULdwwsAvjbnqSgIAAX4cpIhWc2dcBVEaGYfDuhnfZmbmTBqEN2JGxg3kb553RbcbHx+Pv709wcDAhISEEBwdTv359xo8fT15e3nH1YTKZWLx48RmN80QFBwfz888//2e7xYsXYzKZynz9RPbt7rvvZsqUKccb4mn1X/txpL///psWLVp4n48dO5axY8eeqdBERERERP7T9u3buX1cXx54tRsZ9dbiDHdjNky4DzkZ1ymcLd+8zf+Nv10FKRE5LhopdRL+Sv6LRbsXEWGPwN/qT4Q9gkW7FtGhZoczehnfK6+8wqhRo7zP169fT/fu3bHb7Tz++ONnbLtnUnZ2drlvMyUlpdy3eTIyMjJwOp3e56+88ooPoxERERGR6mznzp08/sK9ZNiX4a7rAeyYnE5M+wJoHt2X8ZMmEhwc7OswRaSS0UipfzjcjjIfTve/hYGsgizeXPMmDreDEFsIHsNDsF8w+e583lz7JofyDv1nv6dL8+bN6dKlC2vXrgUgPz+f++67jwYNGhAREUH37t1Zvnx5ifWeeOIJEhMTiy178sknOe+884DCUVlPP/007du3Jzg4mLPPPptly5Z52/7xxx9069aNiIgIGjRowAMPPIDDUbhfkyZNYtCgQVx55ZWEhIQQFxfHxx9/zOTJk6lVqxZRUVFMnjzZ29eRI5z+/PNPevToQXR0NMHBwXTs2LHYdo/XpEmTuPLKKxk5ciTh4eHExcXxxBNPADB58mTmzp3L3Llzad++PQBJSUkMGTKE2NhYIiMjGTNmDOnp6QDMmjWLDh06MHDgQMLCwpg3bx6jRo3ixhtv5MILLyQ4OJg2bdrw3Xffebe/f/9+rr76amrVqkXNmjUZPnw4Bw4cKDXWefPm0aZNG8LDwwkPD6d///6kpKSwfft2evfuDRSOJlu+fDmjRo3yFiU9Hg/PP/88TZo0ISwsjLPPPptvv/3W22+3bt24//776datG8HBwbRs2ZKvvvrqhI+liIiIiFRN+fn5zJkzhz439KH39N70uaEPc+bMIT8/v1i7PXv2MOLmu2h3zf3sIR9PbX8wwLStgLbuy3n9oZ958P8eVUFKRE6KilL/uGvxXWU+3ljzBvDvZXt/HvyTw/mHWZe2jnVp61h/aD2H8w+zPGk54xePL9bvg788WKK/08HpdPLdd9+xaNEievbsCcA999zDl19+yaJFizh48CCXXXYZPXr0YOfOncXWHTlyJNu2bStW8JkzZw7XXnut9/msWbOYO3cu6enptGrVipEjRwKwa9cuLr74YgYNGsTBgwf59ttv+fTTT7n77ru9637yySdcfvnlZGRkMHLkSIYMGUJ6ejp79uzhnXfe4cEHH2TXrl3FYnI4HPTu3ZurrrqKgwcPkpycTHx8PHfddXLH6+OPP6ZXr14kJyfz9ttvM3HiRP744w8eeOABhg0bxrBhw/jzzz8xDIPLLrsMgA0bNrBz506cTmexEWl//vkngwcPJjk5mf79+wMwc+ZMbr75Zg4fPsz48eO57LLL2L59Oy6Xi8suuwyLxcKmTZvYtGkTbrebAQMG4HK5isX4119/ccMNNzBz5kzS09NZu3YtGzdu5MUXXyQhIcFbRMrOzqZjx47F1p06dSpTp07lgw8+IC0tzRvDkTl98803efrpp8nIyGDw4MFcffXV3uKhiIiIiFRfcz9eQJ3Etlx3zwQ2RmwnL8rBhojtXHfPBOoktuXdTxayb98+br17JEPuGcM3ycFYYxry955EjJ0BNMvvz+tPruPee58lNDTU17sjIpWYT4pSy5cv9460qV27Nrfccgs5OTkAzJ07l5YtWxIaGkrjxo156aWXfBFiqQ7kHGDpvqWYTWbMpuKHrmjZnqw9HMgufVTMqbr55pu9I2qio6P53//+x3333cdtt92GYRi88cYbPProozRs2BCbzcb48eNJTExk3rzi813FxsZyySWX8M477wCFRZedO3dy1VVXeduMGTOGpk2bYrVaGTRoEJs3bwYKR/Y0bdqUW2+9FZvNRuPGjXniiSd444038Hg8ADRu3JghQ4ZgNpvp3r07LpeL+++/Hz8/Py6++GKAEkUpq9XKzz//zPXXX4/D4WDHjh1ER0ezf//+kzpWLVq04Oqrr8Zms3HhhRcSExPj3YcjLV++nL///ptXXnmF8PBwQkNDefrpp1mwYAEHDx4EICQkhKFDh2K32wkMDASgX79+DBo0CD8/P0aOHEnbtm2ZN28eK1asYNWqVUyfPp2wsDDCw8N59dVXWb16dYlRa82aNWPVqlW0b9+e9PR09u/fT0xMzHHt82uvvcaECRNo27YtVquVIUOG0K9fP958801vm8GDB9OxY0csFgtXXnkl6enpJCcnn9TxFBEREZGqYe7HCxj/wWrs3W+izg09CUuIoKHFTnhCBHVu6Im9+808+/Ui/vdKP5Lj/iK+QyoWnBhJG7m6aQCvPfAtjzz4PGFhYb7eFRGpAsp9TqnU1FR69erFI488wqJFi0hKSmLgwIFMmDCBgQMHcvPNN/PFF1/QuXNn/vjjD3r37k1sbCyDBg06o3E92+3ZMl8z/1O7iw2K5fw653Mo/xB1gutgNf97+FweF/uy93FZwmXEBsd6lz/S+ZHTFuP06dOLjeA5UkpKCnl5eSQkJBRbnpCQUGKkFMC1117LTTfdxHPPPcecOXMYNGgQISH/3j0wMjLS+3NAQIC34LR79+5St5GXl+ct4tSsWdP7msViASAiIgIoLD4B3v6ObLd48WKuuOIK0tPTadq0KX5+frjd7jKPx7EcGX/RPpTW186dO/F4PMTHxxdbbrPZ2Lt3r7evoycnb9asWbHn8fHx7N+/n927d1OjRo1ixzI0NJSoqCh27txJ7dq1vctNJhMvv/wy8+fPx2Kx0Lp1azIzM49rn8vKw8qVK0s9BkUTTZ7s8RQRERGRyi8/P5/bx9+DvfvNWEL2YIvaRrTbQawplwI3uKM30Tp4C/aAbDyGgckAT5rB0CZO7n1lGjVq1PD1LohIFVPuI6V27NhB165dufXWW7FardStW5cRI0awZMkSdu3axbhx4zj//PMxmUyce+65dO/enSVLlpzxuOwWe5kPP4sfUFhEuLrZ1TQIa8DB3IPe0VEmTBzMPUhCeALDmg/7z37PhOjoaAIDA9m2bVux5Vu3bi1WCCly2WWXYRgG33zzDe+//36xS/eOpX79+iW2sWXLFux2u7cIYjaf+Gn166+/cscddzBv3jz27t3LokWL6Ny58wn3c6Li4uIICAggLS2N9PR00tPTOXToEKtXr6ZNmzZlrrdv375iz3fs2EF8fDz169cnNTWVzMxM72vp6emkpqaWyMNzzz3HDz/8wIoVK9i6dSuffPIJ0dHRxxV3WXkoLdciIiIiIgAffvghmZkZYHHgH7cGf4uDWBzkYcbP5sAemMXBiEzcGJj2GXiWx/HCHd/w3GNPqyAlImdEuY+U6tixI/Pnz/c+NwyDhQsX0rFjxxKFkbS0NH755ReeeeaZMvtzOBzF5skpKgZ4PJ4So3E8Hg+GYXgfJyPYL5hRLUbx5LInSXekE2YPI6MgA5vFxqjmowj2Cz7pvktzZF//Fffo0aO59957adKkCfXq1eP1119n/fr1zJ0717teUR9Wq5Vhw4bx0EMPERwcTJcuXcrc1pH/Dh48mEcffZSpU6dy0003sW/fPh588EGGDh2KzWYrdZ2j96O0/ovyZjabcbvd/PLLL7z00ktYrdZSYynrWB2rbdFrNpuN5ORkDMOgQ4cONG7cmHHjxvHYY4/h5+fHpEmTePfdd9myZUuZfX3wwQeMHDmSzp07M3v2bDZs2MCwYcOoUaMGrVu3ZuzYsUyfPh2TycTtt99Ow4YNOe+88/jll19K7LPJZMLhcPD222/z448/MmTIEG+cAIcPHyY8PLzYfowZM4YnnniCc889lzZt2vDZZ5/x+eef8/XXX5fI9dF5KOu4lPaekX8VfX7oGFVuymPVoDxWDcpj5accVj7z58/HZDbjX3cd1qBM6ricWKyQY/Yj2+LCbhjkY2FLekM2vP4Hfbs2pkaNGspxJaD3Y9VQlfJ4vPtQ7kWpIzmdTsaOHcuGDRuYPXt2sdeSkpLo168frVu3ZtiwYWX0AI8//jgPP/xwieUpKSkl7hzhdDrxeDy4XK4Sk06fiNaRrbkw7kK+2PEFNpON9Px0+jboS+uo1qfU79EMwyh2uVVR7GV5/PHHeeyxx7j44otJT0/nrLPOYtGiRTRs2NC7ntvt9v58zTXXMHXqVB555JES/R65raIYXC4XcXFxfPXVV9x3331MmjSJ8PBwrr76aiZOnIjL5fK+iUpb90hHxuF2u7nwwgsZO3YsXbt2xeVyccEFFzBjxgyuvPJK9u7dW2Y/pfV5dAxH71O/fv0YPXo0CQkJbN68mU8//ZR77rmHJk2a4HA46NixI1988QUWi8X7RjqyL4/Hw3nnncfkyZP5888/SUxM5KuvvvKOcvrkk0+YOHGi9w6HF110EV9++WWJ43HHHXewZs0a4uPjCQoK4vLLL2fKlCnMmTMHl8tF8+bN6dChA3Xr1uWDDz4oFkvRPGJDhgzh4MGDNGvWjI8//pguXbrgcrm8H2RFcR/579HHpeiYpaWl4efnV+bxre48Hg8ZGRkYhnFSowGlYlAeqwblsWpQHis/5bDySUpKwhIGQTW2E2DOJ8paQAFWrB4L4S4XQW6DDZ5AHKEZWEIL22tO0spB78eqoSrlMSsr67jamYzTOaznBKSkpHDllVeSlJTEF198UWx+nD///JOBAwfSrl075s2b550PpzSljZSKi4vj8OHDJe4EkZ+fz86dO2nQoAH+/v6nFH9WQRb3Lr2X9WnraRHVgkfPf5QQW8h/r3iCnE7nGSsUpKWlUbduXbZu3UqdOnXOyDaqotGjRwPw1ltvHfc6ZzKPpyo/P997+eGpvi+qMo/HQ0pKCtHR0ZX+F0R1pjxWDcpj1aA8Vn7KYeVzzYg+5IauJ6NtKJl+Bk3yPbgMG3gMDMOD3exip9XOvgNt2fvyd/Tteg4fffSRr8OW46D3Y9VQlfKYmZlJREQEGRkZx7xLp09GSq1evZo+ffrQsWNHPvvss2IBvvvuu1x33XVMnDiR+++/v8QE00ez2+3Y7SXnaTKbzSWSaDabMZlM3sepCLWHcm3La5m7YS7Dmg0j1H76b4VqGIY3zlON90gOh4Nt27YxdepU+vTpQ926dU9b39XJ8ebkTOXxdCl6P5T2npHidJyqBuWxalAeqwblsfJTDiuHw4e28e3yFwjsehBHph/RLvAzezhotRDlKCxIGcBBPzMJzgLsyZHs8Xi4/PLLldtKRO/HqqGq5PF44y/3otT+/fvp0aMHo0aN4qmnnir2Jf2bb75h9OjRvP/++wwYMKC8Qzth7Wq2o13Ndr4O44Q5HA46depE48aNi83vJSIiIiIiVUdW5j6+/v0Zlh5chtvw4Ge3Ycu00yUrmjjbHqZFWcizeAhyQ44FrIaZKw/ncMB/HX+Hhp7xO6CLiJR7UWr69OmkpqYyY8YMZsyY4V1ev359IiIicLlcDB8+vNg6w4cP55VXXinvUKus0NBQMjIyfB1GpTVr1ixfhyAiIiIi8p9crnxvQapRUB36tLmO5UF5hC57hjinlXNtbpaGmQgE8q3QJcOgXq4Nj+kQLz/3hKZ2EJEzrtyLUlOmTGHKlCnlvVkREREREZEqLStzH2u3LKRT+7EAREQ2ZGDjy4mNakrDBpdgtVpo3Ag+cucyevJDZDuzsV4Ryv5YK8YeF+98ksmnfsHc++CdDL1ioI/3RkSqA5/efc9XfDS3u0iFpPeDiIiISOWWk53EomVT+WnfLxQYLupEt6RevfMB6HrOeOb/vY9vftrOTd0a4mcxM+iqYfTtfwUfffQRcxfPJcmaRK2U+gx7YBiDBg3SCCkRKTfVqihVdPez3NzcY97RT6Q6yc3NBaiwdwcUERERkdLlZifz/fIX+XHvTxQYLgDqBcTg9ri8bRas2s8Xqw8AsHZfBmfViwDA39+f4cOHc/XVV5OcnExMTEyln1hZRCqfalWUslgshIeHk5ycDEBgYGCFvBtaEcMwcLlcWK3WCh2nHFtFzaNhGOTm5pKcnEx4eDgWi8XXIYmIiIjIcXDkZ/D9H8/xw54fyfc4AajrX4M+La6hZbMrMP1TXFqwaj8LVu4H4KqOcd6ClIhIRVGtilIAtWrVAvAWpioywzDweDyYzeYKVcyQE1PR8xgeHu59X4iIiIhIxWc2Wfll38/ke5zUtkdyaYthtGk+xFuMAvhi9QE++3sfAIM71KVnC/29JyIVT7UrSplMJmJjY4mJicHpdPo6nGPyeDykpaURFRWlobSVWEXOo5+fn0ZIiYiIiFRwjvwM/lg1i/M73ILZYsXPHsQVLUYBcFar4cWKUQBfrTnAJ3/tBeDydnW5pGVseYcsInJcql1RqojFYqnwX8Y9Hg9+fn74+/tXuGKGHD/lUURERERORoEji59XTOO7nd+Q7c4nwBZMx7PGANCuzTWlrpOeW8DC1YWX7A04qw59WqsgJSIVV7UtSomIiIiIiFRETkcOS/+awbfbvyDLnQ9ADb8Q7Lbg/1w3PNDG+IubsDEpi76ta5/pUEVETomKUiIiIiIiIhWAx+1i6YqX+GbbQjJceQBE+gXRu8kgzm5zLRarrcx1cwtcBNoKv941igmhUUxIucQsInIqVJQSERERERGpAEwmMyv2LCHDlUeENYjejQdydtsxWP38j7nej5uS+fTvffyvZyJxkYHlFK2IyKlTUUpERERERMQHXM58lq2aSdumgwgMjsFkNtPvrJs4kLaRTmfd8J/FKIAlm1N457ddAPy1+7CKUiJSqagoJSIiIiIiUo7crgKWr3qLrzZ/RJozm0PZB+jbbTIAjRr2pFHDnsfVz9Itqcz+bScAFzevSb82mkNKRCoXFaVERERERETKgcftYsXqWXy58QNSnVkABFv8CQ+MOeG+ft2Wyqxfd2AYcGGzGK7qGIfJZDrdIYuInFEqSomIiIiIiJxhf62azecb3iW5IAOAIIudnvGXcH77m7D7h51QX79vT2Pm0sKCVLemMVx9dj0VpESkUlJRSkRERERE5AzbsP83kgsyCDTbuKj+xXTteNsJF6MADMPg5y0pGAZ0TYxm+DkqSIlI5aWilIiIiIiIyGlkeDysWv8etWo0p1attgD06jiOyHXv0a3j7QQERp503yaTidsubMxPm1Po2bymClIiUqmpKCUiIiIiInIaGB4Pazd8zBfrZrM3P5WzIpoy5rK3AKhRoym9u0466b6TMvKpFVZ4Nz5/Pwu9WtQ6HSGLiPiUilIiIiIiIiKnwPB4WL/5U75Y8za785IBsJmsxATXwfB4MJnNp9T/37sPM33xNi5rU1t32BORKkVFKRERERERkZO0ZevXLFj5KjtykwCwmSx0rXM+PTqOIzgk9pT7X7UnnRmLt+HxGBxIz8MwDF2yJyJVhopSIiIiIiIiJ2lX0t/syE3Cz2ThgthOXHT2HYSE1jktfa/Zm8G0H7fi9hh0iI/kui4JKkiJSJWiopSIiIiIiMhx2rrtWwAaNewJQJcON5NTkEG39jcTFlbvtG1n3f4MXv5xC26PQbv6EVzfpQEWswpSIlK1qCglIiIiIiLyH7bv+IEvVr7Kpqzd1LZHMrHBRZjMZuz+YfS/8InTuq0NBzJ56futuNwGbePCufGCBKyWU5uXSkSkIlJRSkREREREpAw7d/7El3+/wvqsnQBYTGYSwhIoKMjC7h92RraZlJGP0+2hTVw4N3VrqIKUiFRZKkqJiIiIiIgcZd++ZSxY/gLrMrcDYMLEudFtuaTjHUTVaHJGt929aQwRQTZa1A5VQUpEqjQVpURERERERI6Slr6DdZnbMWHi7BqtuKTD7UTHtDhj29uZmkONEDvB9sKvaG3jws/YtkREKgoVpUREREREpNrbv38FqYe30brFVQC0ajaYXslrOLv5ldSs2fqMbntbSjbPfbuZ6BA7/+uV6C1MiYhUdfq0ExERERGRaivpwN98ueIl/j68kQCzHw83uJiAwEhMZjOXdZ9yxre/IzWH577bTL7TTaDNgp9Fd9gTkepDRSkREREREal2Dh5czdcrXmJF2joMDACahDWgoCCTgMDIcolhV1oOz367ifwCN41rhnB7j8bYrZZy2baISEWgopSIiIiIiFQbhw9t4/Pfn2JZ6hpvMap1eCMubXcLdeueW25x7E7L5ZlvN5NX4KZRTDB3XNQYfz8VpESkelFRSkREREREqg2HI9NbkGoRmkCfdjdRr9755RrDnkO5PPPtJnIdLhKig7jjoiYqSIlItaSilIiIiIiIVFmHD21j045FnNv+RgBqxZ7FgEb9aVj7XOLju/okJpvVjJ/FTHyNIO68uAkBNhWkRKR6UlFKRERERESqnPTDO/h22VR+SV6BxzBIqNuZmJotAehx3gSfxlYz1J//692UILuFQJu+kolI9aVPQBERERERqTIyMnbz3bKpLE36A5fhAaBJcF3cbodP40rKyCc120HLOmEARIfYfRqPiEhFoKKUiIiIiIhUerm5qXzz65MsOfAbTsMNQMOg2vRpcx1NGvX2aWwHM/N56puNZOe7uOOiJjSvHerTeEREKgoVpUREREREpEr4NekPnIabBoG16NNqNImN+2Iym30aU3JmPk99vYmMXCd1IgKoGxng03hERCoSFaVERERERKTSyclO4s918+jScRwms5nAwBoMaj6ckMAYmjXp5/NiFEByVj5PfbOJ9NwCYsP9+V+vREL9/XwdlohIhaGilIiIiIiIVBq5uan8sOwFfty7GIfHRVRYfVo0vRyAc9rd4OPo/pWa7eDprzdxOKeAWmH+3N2rqQpSIiJHUVFKREREREQqvLzcQyxe/iI/7PmBPI8TgNr2SKyWijdheEaek6e/3sShnAJqhvlzd69EwgJUkBIROZqKUiIiIiIiUmE5nbn88PuzfL9rEbmeAgBq2cLp03wYbVteXSEu0ztaiN1K45rBmM1wd89EwgNtvg5JRKRCUlFKREREREQqLIvZxvJ9v5LrKSDGFsalTYfQrtVwzJaK+1XGbDZxbecGZBe4dMmeiMgxVNxPchERERERqXYKHFn8+vdrdD5rLH72IMwWKwPbXk9O3iE6tB5VYYtRGblOvt94kAFt62A2mzCbTSpIiYj8h4r5iS4iIiIiItWK05HDL3+/wrfbviDTnYfH4+bC8+4B8E5kXlFl5Dl5+tuNHEjPx+n2cFXHer4OSUSkUlBRSkREREREfMbpzOW3v17nm20LyHDlAhDpF0RoYIyPIzs+mflOnvlmEwfS84kIstE9sXLELSJSEagoJSIiIiIi5c7wePj1z+l8teVT0l05AIRbg7ikUX/OPet6rH7+Po7wv2XlO3n2m03sT88jLNCPe3olEhNa8eMWEakoVJQSEREREZFyZzKbWXfgD9JdOYRZA+jV8DI6tbsRP79AX4d2XLIdLp79djN7D+cRFuDHPb2aqiAlInKCVJQSEREREZEzzu0qYMXqWTSOv5DIyEYA9Gl/G032/Oyd1LyyMAyDF7/fwp5DuYQG+HH3JYnUClNBSkTkRKkoJSIiIiIiZ4zH7eLPNbP5auMHJBdk0Dl5NUMveRmAOnXOpk6ds30c4YkzmUz0bR3L7N92cefFTYgNC/B1SCIilZKKUiIiIiIicsL+Sv6Lt1a+xei2o+lQq0OJ1w2Ph7/XvMOXG+aRVJAOQKDZRs3QqnFnutZ1w3lsYCg2q9nXoYiIVFo++QRdvnw53bp1IyIigtq1a3PLLbeQk5NTrM3vv/+O3W73RXgiIiIiInIMWQVZzFo3i3Xp63h73dtkFWQVe33Nug957MO+zFw1g6SCdALNNi5rcCmPXPEpF553j4+iPjX5TjczFm8jOTPfu0wFKRGRU1Pun6Kpqan06tWLQYMGkZKSwrJly1i2bBkTJkwAwOPx8NZbb9GzZ08KCgrKOzwRERERETkGwzB4d8O77Dy0iQYFTnYc2si8jfOKtdl+8E8OOA4TYPajT/1eTLr8Y3p1eQD/gAgfRX1q8p1upi7awoqdh3j5x60YhuHrkEREqoRyv3xvx44ddO3alVtvvRWAunXrMmLECN544w0AxowZw8aNG5kyZQrjxo0r7/BEREREROQY/kr+i0W7FhFekE9gfjrhZjMLN31IXcOPS9uMBuCis+/AarbRvcOtBAbH+DjiU+NwuXnx+y1sOZhFgM3C6M4NMJlMvg5LRKRKKPeRUh07dmT+/Pne54ZhsHDhQjp27AjA5MmT+e2332jdunV5hyYiIiIiIseQVZDFW2vfoqAgizBHNllmM6mOQyTnHGTamlfJzM8AICi4Fn26PVLpC1IFLg8vfb+VTUlZ+NssjL+4CQ1qVJ67BIqIVHQ+nejc6XQyduxYNmzYwOzZs4HCkVMnwuFw4HA4vM8zMzOBwssAPR7P6QvWBzweD4ZhVPr9qO6Ux6pBeawalMeqQXmsGpTHyscwDOaun8vOjJ1E5Kaz3XCQay4cMeRvGORgMHfDO9zY5iYfR3p6FLg8vPzjVtYfyMRutXDHhY2Ijwqscues3otVg/JYNVSlPB7vPvisKJWSksKVV15JUlISS5YsITY29qT6efzxx3n44YdL7T8/P7+UNSoPj8dDRkYGhmFgNmsSxcpKeawalMeqQXmsGpTHqkF5rHwO5h3kmy0LyMo9QJa7AEwmTECkYaaGx0xKUASLdy+hc3gXYgIq9wgpgM/WpvL3znTsVhPXdIwmhFySk3N9HdZpp/di1aA8Vg1VKY9ZWVn/3QgfFaVWr15Nnz596NixI5999hmhoaEn3dfEiRMZP36893lmZiZxcXFER0efUr8VgcfjwWQyER0dXelPyOpMeawalMeqQXmsGpTHqkF5rHyijWhabIjl2+w9+AM1sBBlsmGzWHE5c3AWHOaSJgNpWb+lr0M9LYZ2juRQwTYGtK1NYq0QX4dzxui9WDUoj1VDVcqjv7//cbUr96LU/v376dGjB6NGjeKpp5465UkC7XY7dru9xHKz2VzpkwhgMpmqzL5UZ8pj1aA8Vg3KY9WgPFYNymPFt2PnYnLz0mjR7AoA/td8JJsO/EG+2Y9YS0DhpSZAktVKg4ICro5oVanzaRiG9/tJaICN/+vdtFpMaq73YtWgPFYNVSWPxxt/ue/l9OnTSU1NZcaMGYSEhBAcHExwcDAtWrQo71BERERERKQUu3b9zIxPr+bZJROZ9/cMnM5cMAzCN3zO/+WZ8TeZyfAUYPK4yTSc2DExOtdNyPpPwTB8Hf5Jcbk9TF+8jR82HvQuqw4FKRERXyr3kVJTpkxhypQp/9muW7duGJX0F5qIiIiISGW0Z89vfPnXdNZkbAXAhIlmEY0pcGTiZ/aDzAO0s4ZykSufhRYnNgPSDejn9qOdNQwyD4DHBRY/H+/JiXF7DF77eTt/7TrM6r3ptI2LIDLI5uuwRESqPJ/efU9ERERERHzv4MHVLPzjWVambwYKi1Eda7Skd4dxRMcccUVD36mYHBlc7cxhzV/Ps+7wFlpGNmHoWXeAXxDYwyplQeqNn7fz587DWMwmbuneSAUpEZFyoqKUiIiIiEg1l5t3iJXpmzFhon1kc3p3vJ2aNVuXbBgUBUFRhACjzrqZt1a+xci2owmp2arcYz4dPB6DN5duZ9mOQ1jMJm7u3ojWdcN9HZaISLWhopSIiIiISDWTlLSSPQf+pONZYwBoEN+Nfnv60LpRH2rFnnVcfbSLaUfdtnWJiYk5k6GeMR6PwcxfdvDH9kOYzSbGdmtI27hwX4clIlKtqCglIiIiIlJNpCSv48vlL7AibS1Wk5kmDXoQFh4PQM8u9/s2uHK2am86v21Lw2QyMbZrAu3qRfg6JBGRakdFKRERERGRKi41ZQNfr3iBP1JWY1B4M6GmoQ1wuvJ9HJnvnFUvgoHt6lAz1J/29SN9HY6ISLWkopSIiIiISBWVkbGbL357kt+TV+LBA0CL0AT6tLuJevXO93F05c8wDJxuA5vVDEDf1rV9HJGISPWmopSIiIiISFVleFiesgoPHpqFxHPpWTfSIL6br6PyCcMwmPvHbval5zGuR2P8/Sy+DklEpNpTUUpEREREpIrISN/Jqk2fccE54wAIC49nUNMhxNZoRkKDHj6OzncMw+C95Xv4cWMyJhNsSsqijSY1FxHxORWlREREREQquYyM3Sxa9gJLk/7AabipG9OKhAYXAtC5460+js63DMPggxV7WLT+IAAjz4tXQUpEpIJQUUpEREREpJLKytzHomVTWXLgN5yGG4CEwFpYzH4+jqxiMAyDj/7cy7frCgtSIzrVp0vjaB9HJSIiRVSUEhERERGpZBz5GXz96+P8tO8XCgwXAPEBNenTejRNG1+GyWz2cYS+ZxgG8//ex9drkwAYfm59uiXG+DgqERE5kopSIiIiIiKVjNnix/KkFRQYLuoFxNCn5TU0TxyoYtQRMvKcLN6UAsDQs+vRvakKUiIiFY2KUiIiIiIiFVxubiq/rXyTbmfficVqw88vkMGtr8NsstCy2RUqRpUiPNDG3b0S2ZqcrYKUiEgFpaKUiIiIiEgFlZ93mMXLX+L73YvI8zgJsodzbvsbAWjTcoiPo6uYDucUEBFkAyAuMpC4yEAfRyQiImVRUUpEREREpIJx5Gfw0/KXWLTrO3I9BQDUsoUTEqRJuo/lqzUHWLh6P3dc1IQmNUN8HY6IiPwHFaVERERERCoIj9vFj78/w3c7vyHbnQ9AjC2MS5sOoV2r4Zgt+vO9LN+sS+KjP/cCsC05W0UpEZFKQL/VREREREQqCJPJzJqkFWS786nhF8KlTa+kfatrsFhtvg6tQlu0/iAfLN8DQL+2tendKtbHEYmIyPFQUUpERERExEeczlx+++tV2jUfQnBILCazmf7tbyUpbSNnt7lWxajj8MPGg8xbthuAvm1i6demto8jEhGR46WilIiIiIhIOXM58/nt79f4ZusC0l05HM45SP8LnwCgQXw3GsR3822AlcTiTcnM/b2wINW7VSwD2tbBZDL5OCoRETleKkqJiIiIiJQTlzOfZSvf5Out8znkzAEgzBpITFi8bwOrhAzDYM3eDAB6tajFFe1UkBIRqWxUlBIRERERKQfL/n6DLza+T5ozG4AwawA9E/py3lk34mcP8nF0lY/JZOKmbg35ffshOjeKUkFKRKQSUlFKRERERKQcbDu4kjRnNsEWf3o26E2X9reoGHUSdqTmEB8ViMlkwmoxc37jGr4OSURETpKKUiIiIiIip5nH7eKvNe9QO6YltWt3AKDX2XcQvf5DunS4Gbt/mI8jrJx+357GGz9v58KmNRl6dpxGR4mIVHIqSomIiIiInCaGx8Pfa97hyw3zSCpIp3V4I27oNweAyMhGXHT+RB9HWHkt23GIN37ejmGA0+3xdTgiInIaqCglIiIiInKKDI+HVevf48t1c9nvOARAgNmPeuGNMTweTGazjyOs3P7cdYjXlhQWpM5vXINrOtXXKCkRkSpARSkRERERkVOwYdNnfLbqDfbmpwLgb/aje91udD/7dgIDNd/Rqfpr92Fe+Wk7hmFwXqMajDovXgUpEZEqQkUpEREREZFTcCBtE3vzU7GZrHSvewEXdryNoOBavg6rSli5J51XFm/D4zE4NyGK0SpIiYhUKSpKiYiIiIgcJ8PjYdOWzzGZLSQ27gNAl/a3kO/M4YJ2YwkOifVxhFVLvtONxzA4u0Ek157fALNZBSkRkapERSkRERERkeOwacsXfLH6TbbnHKCWLZx7E3phtljxswdxadeHfR1elXRuQhSRQTYaRgdjUUFKRKTKUVFKREREROQYtm77li9WvsaWnH0AWE1mmkU1x+nMwW4J83F0Vc/GpExqhvgTEWQDoEnNEB9HJCIiZ4qKUiIiIiIipdiz5zc+XTGVTVm7AbCYzHSu2ZFeZ99BWHi8b4OrojYcyOSFRVuICPLj/y5pRlign69DEhGRM0hFKRERERGRUmTmHGBT1m4sJjOdotvR6+w7iIhs6OuwqqyNSYUFKafbQ63QAILsFl+HJCIiZ5iKUiIiIiIiwO7dS0k9vJ12ba4BoHmTAfRNXkvH5kOIqtHEx9FVbZsPZnkLUi3rhHFz94ZYLWZfhyUiImeYilIiIiIiUq3t2fMbX/41nTUZWwkw+9G08aUEBtbAZDZzyQUP+jq8Km9rchZTF22mwOWhRe1QbuneCD8VpEREqgUVpURERESkWtq/fwVfrniJlembATBhomVEU5zOXB9HVn3sSM3h+e+24HB6aBYbyq0XNsZmVUFKRKS6UFFKRERERKqVtNTNfPbbE/x9eCMGBiZMtItsRu/2t1Ir9ixfh1ethAf4ERboR3xgILf1aKSClIhINaOilIiIiIhUK253gbcg1TYikUvb30rt2h18HVa1FBFkY0Kvptj9zNitmthcRKS6UVFKRERERKq0lOR1bNr5A+effRsAMTVbMjhxMAl1OlG37rk+jq762Z2Wy8GsfDrGRwIQFujn44hERMRXVJQSERERkSopNXUj3yx/gd9TVgHQKO587+V5F5xzpy9Dq7b2HMrlmW83kVvgwmYx0yYu3NchiYiID6koJSIiIiJVyqFDW/lm2VR+S/4bDx4AmofE+zYoYe/hwoJUjsNFgxpBNK4Z7OuQRETEx1SUEhEREZEqISc7ic9/eYxfk//EbRQWoxJD6tGn7Y0kNLjQx9FVb/vT83jmm01k57uoHxXE+J5NCLTpq4iISHWn3wQiIiIiUiVYLHb+TFmF2/DQJLgul7a5nkYNe/o6rGrvQEYeT3+ziax8F3GRgSpIiYiIl34biIiIiEillJW5j2Vr5nBhp3swmc34B0RwZcvRhAbXpEmj3r4OT4CMPCdPf7OJzDwndSMC+F+vRILt+goiIiKF9BtBRERERCqV7KwDLFo2lSX7f6HAcBMd3oDWLa4CoEPbUb4NTooJ9bdydnwk6w9kcpcKUiIichT9VhARERGRSiEnO4kflr/Ej3uXUGC4AKgfEEOQf6SPI5OymEwmruoYR77TQ4DN4utwRESkglFRSkREREQqNKczl29+eZwf9y7G4SksRsX516BPy2to0fQKTGazjyOUI6VmO/hqzQGGnF0PP4sZk8mkgpSIiJRKRSkRERERqdCsFn9WH/wLh8dFHf8o+rQYQatmg1WMqoAO5RTw9NebSM12gMnEiHPr+zokERGpwFSUEhEREZFyk5+fz4cffsi7P71LUu0kau2vxdVdr2bw4MH4+/sXtsk7zNI/X6FLh5ux+4dhMpu5ot3N5OWn06bFUBWjKqhDOQU8/c1GUrMdxITa6dsq1tchiYhIBeeT3+jLly+nW7duREREULt2bW655RZycnK8r5199tkEBgYSHx/Pa6+95osQRUREROQ0m/vxAuoktuW6eyawMWI7eVEONkRs57p7JlAnsS3vfPge3y19jAc/HsCn2xewZMU077qJjfvQttUwFaR8LC3bwa60HO9jb3rh8zV703nws7XsPZRHjWA7d/dqSkSQzdfhiohIBVfuI6VSU1Pp1asXjzzyCIsWLSIpKYmBAwcyYcIEJk+eTO/evbn33nu57bbbWLZsGb169aJhw4b06NGjvEMVERERkdNk7scLGP/BauzdbyKs/t+E1tpIHbeDfQl2/G44m5aufL7c+wQB6WYC7HaibaFEh+nSr4okLdvBuPf+5nCuEwDDAJfLidm8i4NZ+TjdBoE2Cw9e1pxIFaREROQ4lPt/Ne3YsYOuXbty6623YrVaqVu3LiNGjGDJkiV88sknREREMH78ePz8/OjcuTNDhgzhrbfeKu8wRUREROQ0yc/P5/bx92AJCMUSshdb1Dai3Q5qmXKJMucQXm8lrjqbcPsZ5B/MZEjiMO4ftJC2rYb5OnQ5QrbDxeFcJ3aLmVB/K6EBVoLtFjLyCsAAm9VERKANm1Wj2URE5PiU+0ipjh07Mn/+fO9zwzBYuHAhHTt2ZM2aNbRu3bpY+9atWzNz5szyDlNERERETpMPP/yQzMwMoi0O/OPW4G9xEGs4cBkWbCYXhslCmtWEa2dzln68ldY1owirkwfkFesnKthG7fAAAApcHjYlZZW5zfBAP+IiAwFwuT1sOFB227AAP+pFFbY1DIO1+zLLbBvibyW+RpD3+Zq9GWW2DbJbSIgO9j5ftz8Dj6f0tgE2C41i/m274UAmLrdRalt/PzONa4Z4n29KyqLAVXrHNquZxFr/tt1yMIt8Z+ltrRYTzWJDvc+3JmeTV+D2Pj+QkUdegRuzHaweE8H+ViyGhXpRNvYezqVWWAD5TndpXYuIiJTKpxOdO51Oxo4dy4YNG5g9ezb3338/QUFBxdoEBQWRnZ1dZh8OhwOHw+F9nplZ+EeEx+PBU9Zv/UrC4/FgGEal34/qTnmsGpTHqkF5rBqUx8pn/vz5mMxmAuuuITg8hdpOA4sJcrER5XJguEzsIogkWyBBZw1k7qp0NvltKtFPj6YxDD27HgDZ+QU8v6hkmyJdGtVg5HnxAOQ7Xcdse3Z8JDdckAAUFrCO1bZt3XBuvbCR9/lLP2zBVca52Dw2lPEXN/E+n/HjNnKdrlLbNowOZmLvpt7nry/ZTnpeQalt64YHMqlfc+/zWb/s4GBWfqltY0L8eWxgS+/zub/vYvfh3FLbhgfYeGbwv/9B/MHy3WxN+ffv8NwCN8lZ+VhzzNisZlrUDgUKL9lrUjOEPKebvIKq8Xd4daLP1KpBeawaqlIej3cffFaUSklJ4corryQpKYklS5YQGxtLSEgIe/bsKdYuJyeHkJCQMnqBxx9/nIcffrjU/vPzS//lXFl4PB4yMjIwDAOzJvWstJTHqkF5rBqUx6pBeaxcDMNgy9bNdB4QQ269VXhMHky4KXBawQDDsBKDmyTAiNxFtsuKOz2fmABTib4srjySk5MByHa4S21TxOrO97Z1uDzHbGs3HN62bo9x7LYUeNsC1PAHj1F6+wCcxdpG+RsEW0tvG2wu3jbC5sFG6W1Dra5ibcP83BhlxBzm5y7WNsTqKnP/gmyeYm2DzMXbZpnBajZhs4DNDAUOB06XCwzAZKLA6cblcpOWdogAd06p25CKR5+pVYPyWDVUpTxmZZU9QvlIJsMwSh8XfAatXr2aPn360LFjR2bNmkVoaOEw4ddff52nn36azZs3e9ted911OBwO5syZU2pfpY2UiouL4/Dhw95+KyuPx0NKSgrR0dGV/oSszpTHqkF5rBqUx6pBeaw8fvxxEW/Puwd3Yw/OQINUu4lsq4faBW5sBf/+36jd7GKn1c6+A23Z+/J39O16Dh999JEPI5fS7ErL4ZZ3VxIaYCXQZsEwoKDAgc1mx2QqHEmVmedi2tVtqR8V9N8dSoWgz9SqQXmsGqpSHjMzM4mIiCAjI+OYtZlyHym1f/9+evTowahRo3jqqacwmf7935fLL7+cu+++m+nTpzN27FgWL17Me++9x+eff15mf3a7HbvdXmK52Wyu9EkEMJlMVWZfqjPlsWpQHqsG5bFqUB4rtiU//8zrb92Bs24K7kQrJqsNq8uf+ofq4IrawUE/M0X3ZjOAg35mEpwF2JMj2ePxcPnllyu3FZDZbMZkAhNgwgQmo/AnU+FzE2AyVZ2/w6sTfaZWDcpj1VBV8ni88Zf7Xk6fPp3U1FRmzJhBSEgIwcHBBAcH06JFC6Kiovjiiy944403CAoKYsyYMbz00kt069atvMMUERERkRP066+/0mXgKAZOnke65zDuECvmAg+mNS6+m5ZD53QXQw7nYzXM5FvBYgKHFayGmSsP5zPEfx2hoaEMGjTI17six+BwesgtcJFb4CbP6Sa3wE1ugQtHGROoi4iIlKXcR0pNmTKFKVOmlPl6586d+euvv8oxIhERERE5FX/8/jsvvfMQmw8HsdPSCkso/LWvA+ebVtC3+71c/X9j+ODTBQT/8hhxuTbOzXKzNMxEIJBvhS4ZBvVybXhMh3j5uSfw9/f39S5JKYLtViIC/Tic68Th9mAY4HK5sbpdFF38EBHoR7Ddp/dSEhGRSkS/MURERETkpPy5YgUvvP0A7toHcDZx0yQ3gB2/NSQ4Yyf3juzL6BHvYrUW/rk59IqBfOTKZfTkh8h2ZmO9IpT9sVaMPS7e+SSTT/2CuffBOxl6xUAf75WUJSrYzgtDziLbUXgHQY/HQ1raIaKiIr2XaQTbrUQFl5xaQ0REpDQqSomIiIjICVm1ciXTXh1Hdu00XI0Lh8hYPCacSfBwn0ZcP/ox/Pz8Sqw36Kph9O1/BR999BFzF88lyZpErZT6DHtgGIMGDdIIqUogKtjuLTp5PB4C3DnERAVV+rlPRETEN1SUEhEREZHjsnbtWl54+U6ywjfgTrCBxYrV449nl51zG13FTU/djs1mO2Yf/v7+DB8+nKuvvprk5GRiYmJU0BAREammVJQSERERkWPasGED//fEy/y8J5/WkUlEN7RhchuYNuXTsdW13PLEnaXeDVlERETkWFSUEhEREZFSbdmyhSen/R9bDmezIedcrJGwLjeQLqs/5YL213PjS3cTEBDg6zBFRESkklJRSkRERESK2b59O4+/NIHciC04E9zUcVnZ+ksenuQd3NrvPO68ZROBgYG+DlNEREQqORWlRERERASAXbt28fzUcSQHbcbVwAKA2TDhOWDmhg5B3HXbWwQHB/s4ShEREakqVJQSERERqeb27NnDs8/dzQHTz3hq28FkxkIA7LXRNOwi7nrwfkJCQnwdpoiIiFQxKkqJiIiIVFP79+/n/iem8tnK/dQNOUCTbnYwwLw9jyax/bjrvocICwvzdZgiIiJSRakoJSIiIlLNJCUl8fjz97Hp0Bb+OtwdS0xD9rvrUWfZLM5ueTnjHptMRESEr8MUERGRKk5FKREREZFqIiUlhcen3s8B0zLcdVyE1zER+Uc6absOMqxLUx743xqioqJ8HaaIiIhUEypKiYiIiFRxaWlpTJ36f2zJW4JRz4bxz3LzQQu967m5d+pLREdH+zRGERERqX5UlBIRERGpog4dOsRzz09iy6GP8cTbIcyECRvWZCsxrvZMvPNRatas6eswRUREpJpSUUpERESkiklPT+ex519m1vdrCAjycE53GwDmPXnUsZ/L/+54mtq1a/s4ShEREanuVJQSERERqSIyMzN55oUprNq/lN8OXgi1mpIHpPzyB+0Sz+XOu5+jbt26vg5TREREBDjJotS+fft477332LdvHw8//DBLly6ld+/epzs2ERERETkO2dnZPPvSY2w4/A3UcWIPM2jk3sfGTS4ubR7No//3M/Xr1/d1mCIiIiLFmE90hV9//ZXWrVvz/fff8/rrr5Oens7QoUOZPXv2mYhPRERERMqQm5vLM8/cz5j/O4sNgZ/jqVuAx2RgPWyhRWABf856hHdmPKeClIiIiFRIJzxS6n//+x9vvPEGAwcOJCIigri4OBYsWMBNN93ENddccyZiFBEREZEj5OXlMWPGVH7fMB0aWjHqmzCZPPilWwk83JAJtzxJo0aNfB2miIiIyDGdcFFq3bp1DBw4EACTyQTABRdcwN69e09vZCIiIiJSTH5+PtNee5PnP1pMQWANLuxkxm0xYUktIDw3kXE3PUuz5s19HaaIiIjIcTnholRsbCwrV66kbdu23mUbN26kVq1apzMuEREREfmHw+Fg+msv8uvmj/h57wV4YgoLTzt/s9M2IY7bbn6BFi1b+jhKERERkRNzwkWpG264gcsuu4yJEyficrl4//33mTx5MjfccMOZiE9ERESk2nI6nbz6xjR+3jwXc30HpsYG7azb+H11FGfXNPHUUx/RunVrX4cpIiIiclJOuCg1fvx4/Pz8mDZtGm63m0mTJnHddddx5513non4RERERKqd/2/vvsOjKvP3j9/TMumBkIRACL1JBwERFbGj2BBRRCyoa13EVVfFRRFdRf3Z29pW8KtgASsW7CgiSpciRSBAqEkIpE8yM+f5/YGblaUlSnKmvF/XxXUxJ8+cc08+CYSbc84EAgG9+uq/9PlPT8nZKU6mlVFQkqfMpdTKKn35/65Tr1697I4JAADwp9S6lHrkkUd07bXXavTo0XWRBwAAIGoFg0G99vrrmvndBJk2lqx2ThkF5CnzyLklU6MvuVd9jzrK7pgAAACHhbO2T7j//vsVHx9fF1kAAACiUjAY1P9NeUMdT7lIt769WCY1IMvjlKs4oPiVDfTX4/+l1575jEIKAABElFqfKXXhhRfqkUce0VVXXaXU1NS6yAQAABAVLMvSG2++rg/m/kvzt/ZUWeoRckr6ZUmmjmxp6cpLH9Vxxx1vd0wAAIA6UetSasaMGdq2bZvGjh27z8eCweBhCQUAABDJjDF6e9pbenf2U3K1KpPaWeqd/Ku+XuhSW89uPfiPh3XCwIF2xwQAAKhTtS6lpk6dWhc5AAAAIp4xRh9++I7e+vAuqZNHpp1RQJK70qngLr/e+vu5OunEE+VwOOyOCgAAUOdqXUodfzynkAMAANSGMUYff/KJpk77u6zWZQp2dMnhNHJXeWVtSNHFZ/xdZ9x6JmUUAACIKrUupVq1anXAH5jWr1//pwMBAABECmOMZn72ue588lXlOjJ0XFaZ3HEuuSqCcm5068Jz7tHZt5xLGQUAAKJSrUupe+65p/r3xhht2bJFr7zyisaMGXM4cwEAAIQtY4y++PIzTXr/Qa3a1VRbEtrJKWnx+i7qH1ynYWfdq3PvGkYZBQAAolqtS6nLLrtsn23Dhg3TJZdcohtvvPGwhAIAAAhXX3/zlV5+559yNC9UsIOlTsVVyv2pqdIqcnXvdVfq/PPOldPptDsmAACA7WpdSu1Pdna2VqxYcTh2BQAAEJa+//47vfzqzapqU6lA+z3b3AGnAjuMnhzRRxdd+DBlFAAAwO/UupT67rvv9nrs9/v1/vvvq02bNoctFAAAQLj48ccf9a+XblJl060KtndLLo/cQa+snDid2vsaXTLmMrlcLrtjAgAAhJxal1IDBw7cewdut9q0aaOXXnrpcGUCAAAIeQsWLNBtD/1LS3Z71Dt9t1KS3XL6LWlVQCce+zeNeuwqud2H5aR0AACAiFTrn5Qsy6qLHAAAAGFh0cKFemLyXVpf7NIa00vuFOnnnf01YPnXOnXALbrstusoowAAAGqg1j8x9enTR/Pnz99ne5s2bbRu3brDEgoAACDU/LxkiR5/ZZz8jTfL3y6ollUx+vX7IxS7K0e3jThNfxn1vGJiYuyOCQAAEDZqVEqtW7dO999/vyRp6dKluuKKK/b6+O7du1VSUnL40wEAANhsxfLlevq5MSpuvE2BtnvuDeW0HPJvcWrsyVm67i/3yev12pwSAAAg/NSolGrTpo3S0tKUn58vY4yMMXt9PCMjQ6+99lqdBAQAALDD6tWr9dgTf1NR8lIFW8VITpdcxi2zMUa9m5+nv068WbGxsXbHBAAACFs1vnzv4YcflrSnoBo3blydBQIAALDT2rVrdcfEJ/X1uhK1TS5Qi9YxclhGWuNTz44jdeP9tykuLs7umAAAAGGv1veUGjdunNavX69t27YpGAxKkvx+v5YsWaJbbrnlsAcEAACoDzk5OZr49B3KKcrXz0XHyZ2WqpyqRspc+JYG9L5c1z0xVgkJCXbHBAAAiBi1LqUeeughjR07Vg6HQ5JkjJHD4VC/fv0opQAAQNjZtGmTJj55h4qSflGgZVBpllMJc0tUsWWzrjmjj/5+4yolJibaHRMAACDi1LqUeu655zRt2jTFxsbqo48+0rhx4zR69GgNHTq0LvIBAADUic2bN+uJJ27RFvfPCrbwSJKcckjbXBrZOU5/f+llJScn25wSAAAgctW6lNq9e7eGDh2qzZs365577lFWVpaeffZZDRo0SBdffHFdZAQAADhstm3bpkceHavNgS9kZXklh1NOeeTc6lGr+ON129jxatCggd0xAQAAIl6tS6mkpCSVlpYqKytLmzdvliQ1adJEOTk5hz0cAADA4bJjxw7d/dCTmj5/g1ITitTjRK8kyZlTobaNTtUtt/1TqampNqcEAACIHrUupc455xxdcMEFev3119WrVy9NmDBBDodDzZo1q4t8AAAAf0p+fr4efOIurSlcpp/yT5arcTsVGaNdc35Wv66n66YJE5WWlmZ3TAAAgKhT61Lq0Ucf1R133KFgMKgHH3xQ559/vkpLSzV58uQ6iAcAAPDH7Ny5Uw89MV65wR9kZfoV11hqVrldG9cU68J+rXXPbYuUkZFhd0wAAICoVetS6plnntF9992npKQkpaena/Xq1XWRCwAA4A/ZtWuXnnzqLq3c/ZnUKlbmt+3uPLf6pxl9fP9jatKkia0ZAQAA8AdKqfvvv18333xzXWQBAAA4IJ/Pp2nTpmnqt1O1vel2ZW7N1IjjR2jYsGGKjY1VUVGRnnzifq3YPkVWK68U75BDRu4Cj1J93TR2zAPKysqy+2UAAADgN87aPuHCCy/UI488osLCwsMSoKCgQK1bt9asWbOqt82YMUPdu3dXSkqKevfurc8+++ywHAsAAISnKe98qKwOPXTVbbdrVcP1qmhUqZUN1+uq225XVofuOm/U9Wo/+C/61w8FUpZHckjObT413dhJD/3lQz3z8KsUUgAAACGm1mdKzZgxQ9u2bdPYsWP3+VgwGKzVvubMmaNRo0bt9c59ixYt0nnnnaeXXnpJI0eO1A8//KDBgwdrxowZGjhwYG3jAgCAMDflnQ9189tL5T3hOqW0WKzkzFXKClZqS2uvEq/rqY7G6KeCo2WablDQCmjLHKd6d+ymm8Y8oZYtW9odHwAAAAdQ61Jq6tSph+XAkydP1vjx4/XYY4/p/PPPr94+bdo0HXfccbr88sslSQMGDNCFF16of/3rX5RSAABEGZ/Ppxtvvk3eE66XKylXMY3WKT1YqXRnucpcAZU1W6HKSpc6B5pq/iqfjm/m1P976Au1adPG7ugAAAA4hFqXUscff7wkaeHChcrNzdXgwYNVVFRU67dSHjRokEaOHCm3e+8IgUBA8fHxe21zuVz65ZdfahsVAACEuWnTpqm4uEjprkrFZi9TnKtCcW6fctwuWQrKyKVCl0NWmVHxgvd13mX/j0IKAAAgTNS6lNq6davOPPNMrVmzRpK0ePFiHXnkkfriiy901FFH1Xg/mZmZ+91+3nnnaeDAgZo2bZqGDBmiuXPnavr06UpNTd3v+srKSlVWVlY/Li4uliRZliXLsmqcJxRZliVjTNi/jmjHHCMDc4wMzDH8vPfee3I4nYpvtlTJDbcr3uHXbodDRg7FW0ZJVQ7lKFaVSbvlcDr17rvvasSIEXbHRg3w/Rj+mGFkYI6RgTlGhkiaY01fQ61LqTFjxmjQoEFauHChUlNT1a5dOz3wwAP6+9//ru+++67WQf/X0UcfrSlTpujee+/Vtddeq+OPP16XX3655syZs9/1EydO1IQJE/bZnp+fL5/P96fz2MmyLBUVFckYI6ez1vekR4hgjpGBOUYG5hh+tm/fLleK5G60VW5HUA6HkdeSEv0OxQUltyOgrZJMo1y5kvesz8vLszs2aoDvx/DHDCMDc4wMzDEyRNIcS0pKarSu1qXUl19+qSlTpsjhcMjhcEiSrrvuOt1111213dV+FRYWqnPnzlq6dGn1tqFDh6pPnz77XT927FjdfPPN1Y+Li4uVnZ2t9PR0JScnH5ZMdrEsSw6HQ+np6WH/BRnNmGNkYI6RgTmGj/LyAn238Dk1y2qkBas3qmpntjxxBWrs98vh90iSAnLI5QgoyV2h4m0dFCxepcyemcrIyLA5PWqC78fwxwwjA3OMDMwxMkTSHGNjY2u0rtalVFJSknbv3q2MjAwZYyRJu3fvVmJiYm13tV+//vqrTj75ZM2ePVudO3fWm2++qU8//VQLFizY73qv1yuv17vPdqfTGfZDlCSHwxExryWaMcfIwBwjA3MMbRXlhZo1/yl9nfu1Kiy/jj6lld770lLbomZqkrFA2zweNfDvWWsk7fA41dpfJW9eqnItS+eddx6zDSN8P4Y/ZhgZmGNkYI6RIVLmWNP8tS6lzjjjDF100UV6+umn5XA4tGPHDo0ePVpnnnlmrUPuz1FHHaWHHnpI5557rvLy8tShQwd98MEH6tSp02HZPwAACD2+il36dsEz+mrjlyq3qiRJTbwNdXyPs5TSdIMujF2hzN2VerZRrHxuKSEolbkkt3Hqgl1l2ha7QouTk/d6R18AAACEtlqXUo888oguu+wydenSRZLUtGlTDR48WA8++OAfDvGfM67+4/rrr9f111//h/cHAADCx1dzHtTnG2aqLLjnjUsyYxrojCMuUs+uIxU00mlXxCmz4Alll8eoX0xQ36c4FC/J55aOKzJqXh4jy1GoZx57sManigMAAMB+tS6lEhMT9c477yg/P18bNmxQdnb2Ad9JDwAA4FByd61VWbBSGTEpOr3jBTqy66Vyuvb8iPLJz1sV2yhLUwNXa9Ub96u0olDuocna2sQtkxvQ6+8W631Pou68+2+6aOgQm18JAAAAaqPWpZQkzZ07V5MnT9bWrVvVsmVLXXPNNdVnTgEAAByIv7JMcxY/r06tTlNG4z0/O5ze50YdseVH9e52uVzumOq1m3eV66Oft0qSbhx2mrqPOU/Tp0/XlFlTtN29XZn5LXTxXRfr/PPP5wwpAACAMFTrUmrq1Km64oorNGTIEHXp0kU5OTk66qij9O677+q0006ri4wAACDM+f3lmrvoJX227gMVBSrUZ+dqXTb4RUlS48bd1Lhxt32eM/WnTQpaRj2yG6hvq1Q5HA6NHDlSI0aMUF5enjIyMsL+JqAAAADRrNal1D//+U99+OGHOvXUU6u3ffrpp7rjjjsopQAAwF4Cfp9+XPySZq79QLsDZZKkBu4Etc3Yt4T6X5cf01LTFmzWxUc1l8PhqOuoAAAAqGe1LqVyc3N10kkn7bXttNNO08UXX3zYQgEAgPC3YMlkfbjydRX695RRKe44ndbmLB3d6xp5PPGHfH5GUqxuOKFtXccEAACATWp9zvtpp52m559/fq9t7733nk4++eTDFgoAAIS//KINKvSXKckVq/Pbn697hs7QgKP+dtBCyrKMcgrK6jElAAAA7FLrM6UcDofGjBmjyZMn64gjjtCWLVv07bffqkePHjrxxBOr13399deHNSgAAAhdVjCghUtfVYOkLLVrO0iSNLDPjfJ64nXskdcpxptUo/18vSpPb8zbpNO6ZOqC3tl1GRkAAAA2q3Up1aVLl73eaa9NmzYaMGDAYQ0FAADCg7EsLVr2mj5Z+YZ2VBWpeVyG/t76VDmcTsXFp+rE/rfVeF95JT69s2izJCk9yVtXkQEAABAial1KjR8/vi5yAACAMGIsSz+veEMf/zJF2yp3SZLinTHq1vhIBYNVcjtja7c/Y/TqDxtUFbDUITNJA9un10VsAAAAhJBal1KFhYV67rnntGHDBlmWtdfHXnnllcMWDAAAhKbVv36sdxf/S1t8OyVJcU6PTsw+Ucf3+avi49P+0D6/XZOvVdtK5HE5dXn/lrzbHgAAQBSodSk1bNgw5ebm6qijjpLbXeunAwCAMFdWvlNbfDvldbp1QrOBOrHvmD9cRklSYVmVpi3Yc9neeb2ylJFcu7OsAAAAEJ5q3SotWLBAW7duVUJCQl3kAQAAIcRYllau+VC+ymL16n6pJKln15EaUrZN/bpdpoTEzD+3/98u2/P5g2qTkaiTj2h8OGIDAAAgDNS6lGrZsqWKiooopQAAiGDGsrT614/0ybJJWl++XUmuWHXpOEQx3iQ5nE6d1P/2w3asfq0bKXdXuUYd01JOJ5ftAQAARItal1L/+te/NHjwYF1++eVq2LDhXh+79NJLD1swAABgj1/XztTHP7+stWVbJEkeh0t9Gh+pQLBSMUo6rMdyOBw6uk0j9WnZUG6X87DuGwAAAKGt1qXUv//9by1fvlyPPvqoXC5X9XaHw0EpBQBAGNuyZZ7e+en/aU3pnvs7uR1OHdO4r07tO0YpDVoe1mMZY1QZsBTr2fOzBIUUAABA9Kl1KTVt2jQtX75cHTp0qIs8AADAJpYV1JrSzXI5nOqfcaRO7TNGDVPb1Mmx5m/YpTfmbdIlR7dQr+YND/0EAAAARJxal1IpKSlq0aJFXWQBAAD1aOPG2dq4fZEGHDVGkpSdfbSGdxyhTm1PV2pq2zo7brHPryk/bVSpL6DcwnJKKQAAgChV61Jq3LhxuuSSS/T3v/9dGRkZcjr/e7p98+bND2s4AABw+OXmztXHi57V8qJ1cjmc6tzmdDVKay9JOrbv6Do//hs/bVKpL6BmDeM0uGuTOj8eAAAAQlOtS6nrrrtOkvTOO+9I2nMvKWOMHA6HgsHg4U0HAAAOmy1b5umThc/o592/SpIccujIRp3ldLoO8czDZ9GmXZqXUyiHw6FRx7TiXlIAAABRrNalVE5OTl3kAAAAdWRX4Tq9M+c+Ldm1WtKeMqp3o84a1Hu0GjfuVm85yioDen3uRknSoC6ZapmWUG/HBgAAQOipdSnVokUL+Xw+ffzxx9q8ebOuvvpqrVu3Tl26dKmLfAAA4E/yeOL1y+51kqReDY/QGb1HK7NJz3rP8eb8XBVV+JWZEquzuzet9+MDAAAgtNS6lFq5cqVOOeUUJScna9OmTTr77LPVp08fvfPOOzrjjDPqIiMAAKiF/LwVWrjqHQ0acLckKTGpiUZ0vVJN0jspK6uvLZksy0iSHA5p1DGtFOPmsj0AAIBoV+tS6qabbtKdd96p66+/Xg0bNlSrVq00ZcoU3XXXXZRSAADYqCB/pWYueFI/5S+VkVGrJkeqQ7vBkqTePS63NZvT6dCVx7bS4K5NlJkSa2sWAAAAhIZal1I//vijPv30U0l7bnIuSeedd56uuOKKw5sMAADUyM6CNfpswZP6MW+JLFmSpM7JrZWU0NjmZPuikAIAAMB/1LqUSktL04YNG9S6devqbbm5uUpNTT2swQAAwMFVlBfqw9n36Ie8hQqaPWVUp6SWOqPntWrZ8nib0+2xcluxPl+xQ5cc3UKpCTF2xwEAAEAIqXUpdeGFF2rIkCGaOHGiLMvSTz/9pNtuu00XXXRRXeQDAAAHEBOTqJWFqxQ0ljokNdfgHlerdauT7I5VzecPavKcDSoordRnK7y6qG9zuyMBAAAghNS4lNq8ebOaNWumCRMmqLKyUhdeeKHKysp04okn6sorr9T48ePrMicAAFGvqGiTZi9+SacdM1YeT7xc7hgN7/VXuVwxatd2kN3x9vHe4i0qKK1UakKMhvTMsjsOAAAAQkyNS6lOnTqpuLhYHo9Hjz76qB599FHl5+crLS2t+t5SAADg8Csp3qIv5z2h77bNld8ElRyXpgFHjZEkdexwts3p9m9tXom+WrlDknRZ/5aK9bhsTgQAAIBQU+NSyhizz7b09PTDGgYAAPxXWel2fTnvCX27ZY6qTECS1DKusTIbtbc52cFVBSy9MmeDjJGOaZumLlkpdkcCAABACKpxKcXZUAAA1A9jWfro27v1zeZvq8uo5nEZGtzlUnXqMEQOp9PmhAf3wZIt2lHkU0q8Rxf2ybY7DgAAAEJUjUup8vJynXjiiQdd8/XXX//pQAAARDuH06nc3etUZQJqFpumwZ0vVZcjhoZ8GSXtOUtqce5uSdIl/VoowVvr91QBAABAlKjxT4oul0vHHx8aby8NAEAkqSgv1LcLntHR3S5VSoOWkqSzet+o/kU56t5peFiUUf8R43Zq/FmdtHDjLvVs3tDuOAAAAAhhNS6lvF4v77AHAMBhVOkr0rfzn9aXG79QuVWlEt8uDTv1cUlSdvbRys4+2uaEf4zX7VL/Nml2xwAAAECI+1M3OgcAALVXVVmi2Que1ecbZqosWClJyohJUZvGPW1O9sflFpZr1fYSndQxQ04n96EEAADAodW4lDruuOPqMgcAAFHhu5+e1Kdr31dJ0CdJSvMk6YyOF6h3t8vldIXn/ZcCQUv//j5HuYXlKq30a0jPZnZHAgAAQBio8U+/n3zySV3mAAAgKuQXb1JJ0KdGnkSd3v589ek+Si53jN2x/pSZK7Yrt7BcCV63TuzQ2O44AAAACBPh+V+yAACEgYDfp7mLX1Tzxj3VosWeM45P7jtGmWta6ageV8ntibU54Z+3dXeFPlyyVZI0vG+2UuI9NicCAABAuKCUAgDgMAv4fZq35N/69Nf3tCtQpo5bftBffyulUlKa65g+f7U54eFhWUaT5uQoaBl1a9ZAR7duZHckAAAAhBFKKQAADpNgoErzf56kT9dM105/qSQpxR2nrk36yliWHE6nzQkPry9W7tD6/DLFxrh0ydEt5HBwg3MAAADUHKUUAACHwdIVb+ndpf9Wgb9EkpTkitUprU7XcUfeII83weZ0h19FVVAf/rznsr0Le2crNSG874sFAACA+kcpBQDAYVBclqcCf4kSXF6d2nKQjut9g2K8SXbHqjNxMS7dMaijZv9aoOPapdkdBwAAAGGIUgoAgFoylqXFy16Xxx2nrp2HSZL69fyLAlaVju5xlbyxKTYnrB/ZqfEacVRzu2MAAAAgTFFKAQBQQ8ay9PMvb+qTFVO0tbJQaZ4kHdH+LLk9sXJ7YjWw3y12R6xzBaWVKq8MqnmjeLujAAAAIMxRSgEAcAjGsrR85Tv6eMX/abOvQJIU6/SoT5N+MlbQ5nT1xxijV3/YoFXbS3R5/5Y6pi2X7QEAAOCPo5QCAOAg1ud8rekLntSmijxJUozDrROaHa8T+/xVCYmZNqerX9+vLdAvW4vlcTnVJj3R7jgAAAAIc5RSAAAcRCBYqU0VeYpxuHR81rE6qc8YJSY1sTtWvdtVVqW35udKks7tmaXMlFibEwEAACDcUUoBAKLGorxFmrRkkkb1GKXemb33+bixLK1Z96l2Fm1U/97XS5Latz1dF+xco55HnK+k5Kz6jhwSjDH6v7kbVVEVVKu0BJ3aqbHdkQAAABABKKUAAFGhpKpEk1dM1ordK/TqilfVIbWDkmKSqj/+69qZ+vjnl7W2bItiHG51bX9OdQk14KgxdsUOCT/lFGrp5t1yOR0adWwrOZ0OuyMBAAAgAjjtDlBQUKDWrVtr1qxZ1dvefPNNdenSRcnJyWrbtq2ef/55+wICAMKeMUZTV07VhsLValXlV07hKr2x6g1J0vqcr/TUuxfoyR8maG3ZFrkcTvVrfKQcDtv/igwJxT6/pv60SZJ0do+mymoQZ3MiAAAARApbz5SaM2eORo0apZycnOptOTk5GjFihN577z2dc845WrFihU444QS1adNGp5xyio1pAQDhalHeIn258Us1qPIp3rdbDVxOfbr2Q21a/ZF2V+RLklwOp/pnHKlT+4xRw9Q2NicOHYkxbp3VvakWbCjUoM7RdWN3AAAA1C3b/ht48uTJGjFihCZOnLjXdrfbrZiYGFmWJWOMXC6XHA6Hli1bZlNSAEA4K6kq0aTlk1RVVaKUylIZp1vJlSXy+8s1pzxXlnGof0Yv3X36JF142lMUUv/D6XTolE6NdcfpHeV2cfYYAAAADh/bzpQaNGiQRo4cKbd77wjZ2dl66aWXNHz4cAWDQQWDQXXu3FnDhw/f734qKytVWVlZ/bi4uFiSZFmWLMuquxdQD/5TzIX764h2zDEyMMfwZIzRlF+maEPRBjWqKFGBVaVUV4KcQZ+ygpbWxSSqXYvTNLzfnZLEfH+nvCogl9Mhr9tVvc0YY2Oi/+L7MTIwx/DHDCMDc4wMzDEyRNIca/oabCulMjP3fwnAokWLdPXVV2vSpEkaNmyY5s+fr+eff/6AL2jixImaMGHCPtvz8/Pl8/kOa+b6ZlmWioqKZIyR08n/Tocr5hgZmGN42lGxQ5+v/VilZVtVEqyUnFKcCSje6ZGrYreSkjL1Y/4ynbRxuTLiMuyOG1KmLtqhjYU+jejVWC1SY+2Osxe+HyMDcwx/zDAyMMfIwBwjQyTNsaSkpEbrQu7d915//XUNGDBAI0aMkCT1799fs2bN0g033KAPPvhgn/Vjx47VzTffXP24uLhY2dnZSk9PV3Jycr3lrguWZcnhcCg9PT3svyCjGXOMDMwx/Gzfvljzlz0tf9kWlVt+eWXUUC65HU45nG4F/FXyV+3SoPZD1KVFF7vjhpSfN+/W8rxKOeRUo0apykhPtDvSXvh+jAzMMfwxw8jAHCMDc4wMkTTH2Nia/admyJVSXq9XDsfebzXtcrm0YcOGA673er37bHc6nWE/RElyOBwR81qiGXOMDMwxPJQUb9G7s+/Rgp0rZGSUZZxyWpaCTo+yXfF7TouWtN3tVquqKo1o2JWZ/k55VUCv/bhJDjl0WudMtW0cmv/Bw/djZGCO4Y8ZRgbmGBmYY2SIlDnWNH/IvcqLLrpI3377raZOnapgMKiff/5ZTz/9tM455xy7owEAwoDXm6zVu9fJyKhHg/a6K6mr7q5wKc7hUpFVJYcVVLHxyyuHRpUHlfTL+1KI3CspFLw9P1dF5X5lJMfq3J5ZdscBAABABAu5Uqpbt2768MMP9cQTT6hhw4YaMmSIrr76ao0fP97uaACAEFRQsEozvvmHzG/3HozxJmlEz+t0+wmP6arBLysrEFAvd7JODji12wTkM37tNgGdHHCqlztFKt4mWQGbX0VoWLG1SLN/LZDDIV1xTEvFuEPuxwQAAABEkJC4fO9/383nlFNO0SmnnGJTGgBAOCgsXKvP5j2huXmLZclS02XtdWT3yyRJXToN++/CM5+Qo7JII/xlWrboca3Y9au6pLbXRT1vkjwJkjdFcnnseREhxOcP6tUfNkiSTuzYWO0aJ9kbCAAAABEvJEopAABqalfhOn0+/0n9kLdQQbPn7KgOSc2V3rDN/p+Q0EhKaKQkSZf3vF6TlkzSZT1GKalx1/oLHQaqgpayGsRLks7rxWV7AAAAqHuUUgCAsOCvLNP7347TnB3zFPitjGqf2EyDu1+tNm1qdnZtr4xeatajmTIyMuoyalhKjvXoxpPaqrgioFiPy+44AAAAiAKUUgCAsOD2xGljUY4CxlKbhKYa3P0qtW97ut2xwp4xpvpdbx0Oh1LiuZQRAAAA9YNSCgAQkkqKt2jWwud00lF/U3x8mhxOp4b2uUmVlcXq0O5MOcL8bXJDxdsLclVaGdTwPtlK8PJjAQAAAOoPP30CAEJKWel2fTXvKc3aMltVJiCX060zjp8gSWrVcqC94SLMuvxSffHLDhkj9W2Zqq7NUuyOBAAAgChCKQUACAnl5QX6et6T+mbzLFVaAUlSdmyaWjbuaXOyyFQVsDRpTo6MkY5u04hCCgAAAPWOUgoAYLvPZt+nLzd+oQrLL0nKim2kwZ0vUdcjhnGZXh35aOlWbdvtU3KcR8P7Nrc7DgAAAKIQpRQAwHZ5JZtVYfnVxNtQgztdrO6dL6KMqkMbd5bpk2XbJUkj+7VQIveSAgAAgA34KRQAUK8qfUX6bsGz6tr2DGVm9pAknd7nJnXeOl89u46kjKpjgaClV77PkTFGvVum6sgWDe2OBAAAgChFKQUAqBdVlSWavfA5fZEzU6VBnzbvXqdRZ/5bkpSWfoTS0o+wOWF0yCupVLEvoASvWxf347I9AAAA2IdSCgBQp/yVZZqz+Hl9vu5jFQcrJElpniR1zjra5mTRqWmDON13bhdtL6pQcqzH7jgAAACIYpRSAIA68+PCFzRj9VsqCuwpo1I9CRrU7jz17X6F3J5Ym9NFr0SvW20zkuyOAQAAgChHKQUAqDPF5fkqClSogTtBg9qeo349/0IZZZNvVuUp1uNSv9apcjgcdscBAAAAKKUAAIdHwO/TvJ9fUaPk5urQ/kxJ0vF9RivOm6x+Pa+SxxNvc8Lotb3Ipzfnb1IgaJTodatrsxS7IwEAAACUUgCAPycYqNL8nydp5pp3VOAvUbPYNN3e9gw5nE55Y1N0XN8b7Y4Y1YwxmjQnR4GgUeesFHXJSrY7EgAAACCJUgoA8AdZwYAWLn1Vn6x+W/lVxZKkRFes+jQ7VsFgldxOLtMLBV+tzNPavFJ5PU5ddnQLLt0DAABAyKCUAgDU2srVH2j64n9pR1WRJCneGaNTWp6mAb1vkDeWS8NCRV6JT+8s2ixJGtY7W40SvTYnAgAAAP6LUgoAUGtV/grtqCpSvDNGJ7U4Wcf3/qti4xraHQu/Y4zRqz9sUFXAUofMJA1sn253JAAAAGAvlFIAgIMylqVlK6epsqpUfXpeKUnq1ukCXVC2Q727Xqz4+DSbE2J/1uaVatW2EnlcTl3evyWX7QEAACDkUEoBAPbLWJZWrHpHH694TbkV+UpwedXtiPPljU2Rw+nUgKPG2B0RB9GucZJuG9RRO0srlZHM/b0AAAAQeiilAAB7MZallWs+1MfLJmljRZ4kKcbh1rFNj5Exls3pUBsdMpMkJdkdAwAAANgvSikAQLXc3Lma9tPDWl++XZIU43Dp+KxjdVKfMUpMamJzOtTE8i1Fykj2KiOJs6MAAAAQ2iilAADVXC6Pcsp3yONwaUCTo3Vy35uUlJxldyzU0O7yKj3/7ToFLaPbBnVUq7QEuyMBAAAAB0QpBQBRbN26L7Rxx2Kd2P82SVLTpr11cefLdESb05TSoKW94VArxhi9NnejKqqCatEoQc1T4+2OBAAAABwUpRQARKH1OV/r4yUvaHXJJjnkUNe2g5We0VmS1O/Ia2xOhz9i/oZdWpK7Wy6nQ6OOaSmXk3fbAwAAQGijlAKAKLJx42x9vOg5/VKyQZLklFNHZ/RUTAyXeYWzYp9fU37aKEka3K2JsjlLCgAAAGGAUgoAokBh4Vq9PfseLS9aJ0lyyKF+6d11Wp8xSkvraHM6/Flv/LRJpb6AmjWM0+Cu3JAeAAAA4YFSCgCiQGxMstb+dqlen7QuOr33mOrL9RDelm0u0rycQjkcDo06ppXcLqfdkQAAAIAaoZQCgAi0desCLVz9vs48/l45nE7FJ2ZoZI9r1SS9ixo37mZ3PBxGHZsk6azuTSVJLXm3PQAAAIQRSikAiCDbty/Rpwue1qLClTIyavtrHx3R4RxJUo8uI2xOh7rgcTl1bs8su2MAAAAAtUYpBQARIG/Hcn264Ckt2LlcRkaS1KNBezVMaWFzMtSVvGKfUhNiuFwPAAAAYYtSCgDCWEV5oabPGqt5Bcuqy6iuKW11Rq/rlZ19tM3pUFcqqoJ6+LPVSop164YT2iot0Wt3JAAAAKDWKKUAIIx5vcnaULxRRkadk1trcK/r1Lz5sXbHQh2btjBXu8qq5HE5lBTLX+UAAAAIT/wkCwBhZFfhOs1a/ILOPHa8PN4EOV1uXdh7jGLc8WrZ8ni746EerNxWrG9X50uSLuvfUl63y+ZEAAAAwB9DKQUAYaBo9wZ9Nu8JzdkxX0FjqeHi5zWw3y2SpPZtT7c5HeqLzx/U5DkbJEkDO2aoY2ayvYEAAACAP4FSCgBCWFHRJn0570nN3v6jAsaSJLVLyFLzxj1tTgY7vLtoiwpKK5WaEKNhRzazOw4AAADwp1BKAUAIMpal97++Xd9tmyu/CUqSWic00eBuV6pDu8E2p4Mdft1Roq9X7ZC057K9WA+X7QEAACC8UUoBQAhyOJ3aUbZNfhNUy7jGOrPbFerQ7kw5nE67o8EmSbEetU5PVJOUWHXJSrE7DgAAAPCnUUoBQAgoK92ur+c/o2O7j1LD1DaSpHP6/E3HFW9Up/bnUkZBmSmxumNQR/kty+4oAAAAwGFBKQUANiovL9A3857SN5tnyWf5VV5VrAtPe0qS1KTpkWrS9EibE8Ju/qAlj2tPKel0OuR1ctkeAAAAIgOlFADYoKK8UN8ueFpfbfpKFZZfktTUm6ojmh1jczKEEn/Q0v0fr9QRTZJ0bs8sed0UUgAAAIgclFIAUM++mfuIPl03Q+VWlSQpM6aBzjjiIvXsOpLL9LCXj5duU25huXaXV+mMrk0opQAAABBRKKUAoJ7tLs9TuVWljJgUndFxuHp1HSmniz+OsbfcwnJ9vGybJOnifi2UFOuxOREAAABwePGvIACoQ/7KMs1e+KxaNe2rVi0HSpJO7jNGWant1bvb5ZRR2K9A0NK/v8+RZRn1atFQvVs0tDsSAAAAcNjxryEAqAP+yjLNWfy8vlj/sYoCFWq/fb5u/K2USkrOUt+eV9kbECHtsxU7lFtYrgSvWyOPaiGHw2F3JAAAAOCwo5QCgMMo4Pdp7uIX9dnaD7U7UCZJauhOUO/sgTKWxT2jcEhbd1fogyVbJEnD+2YrJZ7L9gAAABCZKKUAoAYW5S3SpCWTNKrHKPXO7L3fNYuXvq53l0/Wrt/KqAbuBJ3W9mwd3fNquT2x9RkXYSyvpFIet1OdM5J0dOtGdscBAAAA6gylFAAcQklViSavmKwVu1fo1RWvqkNqByXFJO2zrqKqWLsCZUpxx+nU1meqf89r5PEm2JAY4axHdgPdd04XOR3isj0AAABENNuvIykoKFDr1q01a9YsSdK1116rxMTEvX65XC6deuqp9gYFEJWMMZq6cqo2FK5Wqyq/cgpX6Y1Vb8gKBjRv8ctasmxK9dq+3a/Q8I4jdM/QGTq+380UUvjDUhNi1CA+xu4YAAAAQJ2ytZSaM2eO+vfvr5ycnOptzz//vEpLS6t/ffzxx0pISNBDDz1kY1IA0WpR3iJ9ufFLNajyKd63Ww0qffpg5Vu66a1T9H/L/q13l09SwO+TJLk9sTq272jKKNSaMUYvfrdOyzYX2R0FAAAAqDe2lVKTJ0/WiBEjNHHixAOuycvL0/Dhw/XEE0+oZ8+e9ZgOAPZctjdp+SRVVZUopbJURU6HdlQVqqAiX0v9u+RxuHVss4EyVtDuqAhzs1bn66f1hfrXt2tVWhmwOw4AAABQL2y7p9SgQYM0cuRIud0HjjBhwgS1aNFCo0aNOuCayspKVVZWVj8uLi6WJFmWJcuyDl9gG1iWJWNM2L+OaMccw5MxRlN+maINRRvUsLxIa02lfM499/eJM0YOT5zadBiqk3vdKEnMN0yE4vdjQWml3l6QKyOjIT2zFO9xhlS+UBSKc0TtMcfwxwwjA3OMDMwxMkTSHGv6GmwrpTIzMw/68Y0bN+rll1/WF198cdAbvU6cOFETJkzYZ3t+fr58Pt+fzmkny7JUVFQkY4ycvI182GKO4WlHxQ59s/EbeQIBuarK5HM65JTUyDiVZjm1w5Oi2VvmakCjE5URl2F3XNRQqH0/GmP04tytKi6rUKvUWHVpaJSXl2d3rJAXanPEH8Mcwx8zjAzMMTIwx8gQSXMsKSmp0bqQffe9SZMmqXXr1howYMBB140dO1Y333xz9ePi4mJlZ2crPT1dycnJdR2zTlmWJYfDofT09LD/goxmzDG8GMvSL2ve166iTTqhxQn6aPkkeWXUzBGjBLnlcbkU8JfJX7VLg9oPUZcWXeyOjFoIte/H2b8WaGNxUInxsbr+5E5qnBJrd6SwEGpzxB/DHMMfM4wMzDEyMMfIEElzjI2t2c+1IVtKTZ8+XZdeeukh13m9Xnm93n22O53OsB+itOftwCPltUQz5hj6jGVp1a8z9PHSSdpQsUMeh0u3HnmrlldVKdftVjNn7J7TaSVtd7vVqqpKIxp2ZaZhKFS+H3eVVenthZvlkENDejZT04bxtuYJN6EyR/w5zDH8McPIwBwjA3OMDJEyx5rmD8lSqri4WCtWrFD//v3tjgIgwhnL0pp1n+rjn1/W+vLtkiSPw6UBTY5Wg7VfaVR5UA8mulVkVSnZcqjYaeSVQ6PKA0r65X2p+dHSQS4xBg7kp5xC+aqCapWWoFM7NbY7DgAAAFDvQrKU2rhxoySpa9euNicBEMm2b1usN3+4X2vLtkiS3A6njsvsp5P7jlFKYhNp+hXq5U7WyQGfZrj8ijHSbiOdHfSolztFKt4mWQHJ5bH5lSAcDeqSqfSkGGWmxMnppNgEAABA9AmJUsoYs9fjrl277rMNAA43rzdZG8q3yeVw6pjGfXRa35uU0qDlfxec+YQclUUa4S/TskWPa8WuX9Ultb0u6nmT5EmQvCkUUvhTjmyRancEAAAAwDYhUUoBQH3I2TBLqzbO0unH3yNJapjaRpd2u0atso9Rw9Q2+z4hoZGU0EhJki7veb0mLZmky3qMUlJjzuLEH/f5iu3q16aRkmMpNAEAABDdKKUARLxNm77Xx4v+pRXF6yVJnTaepBYtjpMk9ep+6DdUkKReGb3UrEczZWRk1FlORL4FGwr11vxczVy+XQ+c11WxHpfdkQAAAADbUEoBiFi5uXP1yaLntKxorSTJIYeOSu+mpKQmNidDNCqtDOj1H/fcM/G49mkUUgAAAIh6lFIAIk5xUa7e/nacluxeI2lPGdW7URed3vtGZTTuYnM6RKs3521SiS+gpg3idGa3pnbHAQAAAGxHKQUg4sTFpiqndLMccqhX6hE6vfdoZWb2sDsWotjPubs1d91OORzSqGNayuNy2h0JAAAAsB2lFICwt2PHUs1Z9n8694QH5XS55fEmaOSRN6pBUjM1aXqk3fEQ5cqrAnp17gZJ0qmdM9U6PdHeQAAAAECIoJQCELby81bo0wVPan7BchkZNVs6WX17XiVJOqLDOTanA/b46OdtKir3KyM5Vuf2yLI7DgAAABAyKKUAhJ2CglX6bP6T+jH/ZxkZSVLXlLbKyuhmczJgX2d1b6rKoKV+rVIV4+ayPQAAAOA/KKUAhA1/ZZmmf3O75uYtliVLktQ5uZXO6HmdWrQ4zuZ0wP7Fxbh0Sb8WdscAAAAAQg6lFICw4fbEaVvZVlmy1DGphQb3vFatWg60OxawX2vzStQmPVEOh8PuKAAAAEBIopQCELKKdm/QVwue0Wn9blVCYqYcTqeG9v6b/IEKtW1zqt3xgANavb1ED89cpU5NkzXmpHZy8257AAAAwD4opQCEnOKiXH0x7wl9v/0n+U1QnvlenXXC/ZLEZXoIeZWBoCb/kCNJSkv0UkgBAAAAB0ApBSBklBRv0Zfzn9R3W3+Q3wQlSa3jM9W+WX+bkwE198HircorrlSD+BgN693M7jgAAABAyKKUAhASPpp1l77OnaUqE5AktYjL0OCuo3RE+7PlcHKmCcLDuvxSff7LdknSZf1bKD6Gv2YBAACAA+GnZQAhoahip6pMQNmxaRrc5VJ17jiUMgphpSpgadKcHBkjHd2mkbo1a2B3JAAAACCkUUoBqHfl5QX6dv4z6t7uTDVt2luSNKjPTeq6Y7G6HjGMMgph6aOlW7Vtt0/JcR4N79vc7jgAAABAyKOUAlBvfBW7NGv+0/p601cqt6q0pShHV539qiSpUVp7NUprb3NC4I/rnt1Aizbt0pCezZTo5a9XAAAA4FD4qRlAnav0Fem7Bc/qiw2fqdyqkiRlxjRQrxYn2ZwMOHzapCfqnrM68257AAAAQA1RSgGoUz/Mf1Yfrpmu0qBPkpQRk6LTO16gI7teKqeLP4IQ/sqrAtU3NKeQAgAAAGqOfxECqFMVVSUqDfqU5knS6R3OV+9ul8vljrE7FnBYbN5VromfrNIZXZvojK6ZcjgcdkcCAAAAwgalFIDDxu8v19xFL6hRSgt17nieJOm43jcoIbahene7TG5PrM0JgcMnaBlNmrNBPn9Q6/NL7Y4DAAAAhB1KKQB/WsDv04+LX9LMtR9od6BMTb2p6tT+XDmcTsV4k9TvyGvsjggcdp+v2K4NBWWKi3HpkqNbcJYUAAAAUEuUUgD+sIDfp3k/v6KZv76rQn+ZJCnFHadjW54qywrI5eQyPUSm7UU+vb9kiyTpor7N1SCer3UAAACgtiilAPwhy3+Zpuk/v6QCf4kkKdkVp1PbDNYxPa+Vx5tgczqg7liW0aQ5OQoEjTpnpah/m0Z2RwIAAADCEqUUgD+swF+iRFesTmk1SMcdeb1ivEl2RwLq3Ner8rQ2r1Rej1OXcdkeAAAA8IdRSgE4JGNZWrj0/1QVKFf/3tdLkjp3HKoR5fk6ssvF8sam2JwQqD+WMXI5HRrWO1uNEr12xwEAAADCFqUUgAMylqUly6fq45VTtb1yl+KdMerZabji4lPlcDqrCyogmpzaOVM9mjdQOoUUAAAA8KdQSgHYh7EsLf3lbX284jVtrSyUJMU5PToh+wQ5nS6b0wH2MMZUX6qXkRRrcxoAAAAg/FFKAdjLxo2z9eZPDyvXVyBJ8jrdOqHZQJ3Yd4zi49NsTgfYo7CsSv+atVbD+zZXm/REu+MAAAAAEYFSCsBevN4kbfbtVIzDrROaDdCJfUYrITHT7liAbYwxevWHDVqfX6ZpCzbr9kEduLk5AAAAcBhQSgFRzFiWVv/6kTblLdWpx42TJGVm9tBl3a5WxzanKTGpic0JAfv9sG6nlm8pktvl0GX9ebc9AAAA4HChlAKi1K9rZ+rjn1/W2rItcsih7u3PVuPG3SRJvXtcbm84IETsLq/SG/M2SZLO6ZGlJilxNicCAAAAIgelFBBl1q37Qh///KLWlG6WJLkdTh2beZTi4xrZnAwILcYYvTZ3oyqqgmrRKEGndeYyVgAAAOBwopQCosTOgjWaOvsurS7Zc9aHy+HUMRm9dWrfm9SgYSub0wGhZ/6GXVqSu1sup0Ojjmkpl5PL9gAAAIDDiVIKiBJx8anaVLZNLodT/dJ76rS+Nyk1ta3dsYCQtWjTLknSmd2bKjs13uY0AAAAQOShlAIiVG7uHC1Y/b7OPfEhOZxOxcen6bJeo9Uko7sapbW3Ox4Q8q4Z0Fo9sxvoyBYN7Y4CAAAARCRKKSDCbN78oz5Z9KyW7l4rSWq/+j11PmKoJKlLp2F2RgPCisPh0FGtudcaAAAAUFcopYAIsXXrAn2y8Bkt2bVakuSQQ70bdVZGo442JwPCR2llQJ8s26azuzdVrMdldxwAAAAgolFKAWGuvLxAb319uxYVrpSRkUMO9WzYUWf0Hq3MJj3tjgeElbfm5+qHtQXaXFium0/tYHccAAAAIKJRSgFhLi42VVvKtsnIqEeD9jr9yBuUldXX7lhA2Fm2uUg/rC2QwyGd3aOp3XEAAACAiEcpBYSZgvyV+nrxCzr3+PsU402Sw+nUiL63yOOOV3b20XbHA8JSRVVQr87dIEk6+YjGapuRZG8gAAAAIApQSgFhYmfBGs2c/4R+yv9Zliw1WvCsTjrmDklS61Yn2ZwOCG/TFuZqV1mVMpK9GtIry+44AAAAQFSglAJC3K7Cdfps3hOam79IQWNJkjoltVTbZsfYnAyIDCu3Fevb1fmSpMv6t5TXzQ3OAQAAgPpAKQWEKGNZmv7lLfp+x7zqMqpDUnMN7nE1Z0YBh4kxRtMXbpYkDeyYoY6ZyTYnAgAAAKIHpRQQohxOp4p8hQoaS+0SsnRG96vUru0gu2MBEcXhcGjMye30wZKtGnZkM7vjAAAAAFGFUgoIESXFW/TlvCc0oMdf1CitvSTpzL5/03ElW9Sh3WCb0wGRKznWo0v6tbA7BgAAABB1KKWAOuLz+TRt2jRN/XaqtjfdrsytmRpx/AgNGzZMsbGx1etKS7bpq/lP6tst36vKBFURKNeIQc9KkjIzeygzs4dNrwCIXFUBS6t3FKt7dgO7owAAAABRy2l3gIKCArVu3VqzZs2q3rZu3TqdfvrpSkpKUkZGhm688Ub5/X77QgK1NOWdD5XVoYeuuu12rWq4XhWNKrWy4XpdddvtyurQQ1PfnaGy0u2a8c0/dPf7F+iLzd+qygTVPC5DPVqeYnd8/GZnaaU27iyr/rV5996Pd5ZW2h0RNbC/OU6ak6MHP12pp7/6lTkCAAAANrH1TKk5c+Zo1KhRysnJqd7m8/l06qmnatCgQZo2bZqKi4t1wgknqEmTJho7dqyNaYGamfLOh7r57aXynnCdUlosVnLmKmUFK7WltVfOq4+Wb2Mvvf3TS/q+4F45Y/d8CzaLTdOZXS5V545D5XDa3hVDe4qMMW8u1q7yPYW4MVIg4JfbvUkOx541DeM9enJ4TzVK9NqYFAezvzmWV1apoCwgSdpeXKmfcnYyRwAAAMAGtpVSkydP1vjx4/XYY4/p/PPPr97+0Ucfyefz6YknnpDH41FiYqJmzpwpx3/+FQiEMJ/Ppxtvvk3eE66XKylXMY3WKT1YqSaOclUFJV+j9aramS6PVyooLVK35DY6s8sl6t5pOGVUiCmtDGhXuV9el1Nej1NGUlWVpZgYtxySKv2WdpX7VVoZoMwIYfvM0Uh5xRVyOx1KiXOrYZyHOQIAAAA2sa2UGjRokEaOHCm3e+8I8+bNU7du3XT77bfrzTffVExMjK644gqNGzfOpqRAze05u69I6a5KxWYvU6yrUo1NpXa6nGpoVSrf4VGw+TItXneUmixZpf5nX6wGmedoY2HFXvuJcTvVtEFc9ePcwnIFLbPfY3rcTmX9bu2W3RXyB6z9rnU5HcpOja9+vHV3hapquHZ7kU8+f3C/a50Oh5o3+u/aHcU+VVTtf60ktUxLqP59XrFP5QdZ26JRfHUpnV9SqbLKwAHXNmsYJ7drT7m3s7RSJb4Dr81qGCfPb2sLy6pUXLH3JcJbfvvcxLicivW45HBILuOS0+VUIGj2lFQBS5t3Vcj8bjSZKbGK9bgkSUXlfu0qrzpght+vLfb5VVh64LUZyV7Fx+z587LE59fOg6xNT/IqwbtnbWllQAUlB748LS3Jq8Tf1pZXBZRXfOC1qYkxSo71SJJ8/qC2F/kOuLZhfIxS4mu2tkG8Rw3iYyTt+Zxu3V1xwLXJcR6lJuxZ6w9a2rLrwGuTYv/790uM2ynJod0VVQpaRl6PSy3TElQVsFQZ3P/3AAAAAIC6ZVsplZmZud/thYWF+vLLL3XccccpJydH69at0xlnnKG4uDj9/e9/32d9ZWWlKiv/+4+o4uJiSZJlWbKs8P6HhmVZMsaE/euIJu+9954cTqdim62QJ7FIjR0V2hIjBeRUvGWUVV6l9QlFqszcqNUFF+qVJSVa6l6xz35aNkrQuMFHVD9+8qs1KizbfwnRJCVO953Tufrxc9+s1bai/f9DvVGCVw8N7Vr9+OXZ67VhZ9l+1ybFevT4Bd2rH0/+IUdrdpTsd22My6nnLu5V/XjKjxu1fGvRftdK0suX9q7+/bSFuVq4cdcB1z47oqe87j3FzfuLN2vu+p0HXPvYBd2rS5OPl27VrDX5B1z74HldlfbbmTGfr9imz3/ZsdfHy6uC2lZUIbfTqU5Nk+V1OyUZ7Syt1I7iSgUto4Bl6emvf1V8jKv6ef844wi1+q10+35tvt5ZtPmAGf5+agd1yEySJM1bv1NT52064NobT2ynbs1SJEmLN+3S5B82HHDtNQNaq0/LVEnSii279cJ36w+4dlT/ljqmbZokafX2Yj399doDrr34qOY6oUOGJGldXoke/WLNAdee36uZBnXZ8+f85l3leuCTlQdce3b3pjq7e1NJ0o7iCt370b7fE/9xWudMDTuymSSpsLTyoGsHtk/Xce3SZIwUtIzWF5RWfywrJU4up0NGey7pi4S/M6IJfz9GBuYY/phhZGCOkYE5RoZImmNNX0PIvftebGysMjMzdeedd0qSOnXqpBtuuEFvv/32fkupiRMnasKECftsz8/Pl8934P+ZDweWZamoqEjGGDm5tCssbN++XbHpLqU2WSmnp1wVCkpyyGOkhKBDia4qbTFxMo1yJWec/KVxinPu+83qClYqLy+v+nGMCex3nSS5raq91rqtqgOujZF/r7WuYOUB13oV2GutM3DgtTFOs9daR8B3wLWS9lprqioOurYgP7/6jCbrEGt3FhTI91tBFKwsP+jawp07ZZXv+SMw4CvbZ63ltOR0SE6Hkb+qSo6gQ/5AQFbQyOUwMg4jp0OKdVqKc/738uKiXYXKs/YUfZVlJQfNULx7l/KcewrEitLig64tKdqlvJg9BXx5SelB15YVFykvL/Db7/d9bb9XXlqsvLw9Hy8tOvjnrKK0WP8ZXXHRwWfhKytRXt6euRUVHfhrR5KqykurvyZ2lR7461eS/BW/W1vuP+jaYGW5du4sVCDgl99hyeXYc0pbUqxDcS5LlZVVqvIHFQgEtXNnoeKC+y9oEXr4+zEyMMfwxwwjA3OMDMwxMkTSHEtK9n9Cw/9yGGP2f01QPXI4HPrmm280cOBAPf3007r33nu1Y8eO6iFMnDhRH330kebMmbPPc/d3plR2drZ27dql5OTkensNdcGyLOXn5ys9PT3svyAjnTFGM2bM0LNT71JaP6d2JBoVeywlBS2l+J2K8e8pLbzOgDa4vdqyrYc2P/OFzjz+KE2fPt3m9NifjTvLdMPUJUqOcys+xiVjpKqqSsXEeOVw7DmTqrgioGdH9FCLRgmH3iFswRwjE38/RgbmGP6YYWRgjpGBOUaGSJpjcXGxGjZsqKKiooN2MyF3ptQFF1ygu+66S2PHjtW9996rdevW6dlnn9U//vGP/a73er3yeve9Oa3T6Qz7IUp7CrtIeS2RyBijTz/9VHfffbcWLlyo489tL8vjVTOfW2WWT8UOR3UhZSTt8DjV2l8lb16qci1L5513HrMNUU6nUw6H5JDkkENymD2/c+x57JDkcETOnzWRijlGLv5+jAzMMfwxw8jAHCMDc4wMkTLHmuYPuVKqcePG+v7773XrrbeqSZMmSk5O1l//+ldde+21dkcDqhlj9PEnM/T6p49oa+52LVz4qyRp9oy1GhjbXcObNFLThuv0bHqCfG4pISiVuSS3ceqCXWXaFrtCi5OT93rnSYSmSr8lKbDnxub+oIKOYPW77yF8MEcAAAAg9IREKfW/VxB26dJFM2fOtCkNcGDGGH3+xUxN/vBBOVrsltXeUrOWcXJ/5lKXI7pqwoQJKq0KKOmHicouj1G/kqC+T3EoXpLPLR1XZNS8PEaWo1DPPPagYmNj7X5JOIBEr1sN4z3aVe5XZdCSMVIgEJQ7GNBvbwaohvGe6neuQ2hijgAAAEDo4qdwoIa++vpL/fud++VoUahg+z1nV7gDTgU3JGjKa6/r/KEXVJ+iOD1YoVH3jVepv1Tuocna2sQtkxvQ6+8W631Pou68+2+6aOgQO18ODqFRoldPDu+p0so9Nwy3LEs7dxaqUaPU6jknet1qlLjv5cMIHcwRAAAACF2UUsAhzJkzR0/8e7xiOuQr0OF3ZVROvAYffZ0uumnkPtfLnn/hxTrznKGaPn26psyaou3u7crMb6GL77pY559/PmdIhYlGid7qssKyLMUFy5TRKCHsr++ONswRAAAACE2UUsAB/Pjjjxo/frw+//xzHXFkU3XpkSpX0CErJ06n9r5Gl4y5TC6X64DPj42N1ciRIzVixAjl5eUpIyODfwQDAAAAAPAbSingf8z76Sc9/drdKijerM8//0WStHLhVrXplKWzT7hCox67Sm433zoAAAAAAPwZ/Msa+M3CBQv05Kt3Kdh0m/ztg0oxLqV/laKEmIa6++67dckll1BGAQAAAABwmPAvbES9n5cs0WOvjFOg8Wb52wUlSS7LIWujV3ePu0fXXHWDPB6PzSkBAAAAAIgslFKIWr/88osmPj5OVov18rfdu4zq2/oCXf/gjfJ6eUcuAAAAAADqAnddRtRZvXq1RowYoS5duujTGV8qmGTJaRxybozVkRquVyfO1t9G/51CCgAAAACAOsSZUogaa9eu1YPP3q6dVb/q/TeWSZJ27ihR3rfSqceerzH//Lvi4uJsTgkAAAAAQHSglELEW79+vR58+g6VNlijQKugPJK6HNVM29f5dPvtt+u6665TQkKC3TEBAAAAAIgqlFKIWBs3btSDT92h4uSV8rfac88op3FImz0ads4lunn0nUpMTLQ5JQAAAAAA0YlSChFn8+bNeuDh8drdcJECLQKSfrt52maP2iedrFvvGqfk5GRbMwIAAAAAEO0opRAxtm7dqgcffFAvvPCCglZAF0zsIock51aPWsUP0G133qMGDRrYHRMAAAAAAIhSChFgx44dmvj4P7TFv1DvPrdUVtCSJK37tEIDjzlNt982QampqTanBAAAAAAAv0cphbCVn5+vB5+4S1sdPynYNCCHpAHnttO8T3M1evRo3XrrrUpLS7M7JgAAAAAA2A9KKYSdnTt36sHH79Zm6wdZmQGZ37a78tzq2f5ovfXcP5WRkWFrRgAAAAAAcHCUUggbu3bt0iOPPqz1wc9kNfFXl1HuPLfS/b009m/3KzMz09aMAAAAAACgZiilEPKKior0xBNP6PHHH1dRUZFGTOiuoCR3gVuNfN11x5j7lZWVZXdMAAAAAABQC5RSCFklJSX6f0/+U6uLvtDnr65RUVGZJGnhewU68eSBGnvjRGVnZ9ucEgAAAAAA/BGUUgg5paWleuyZifqlcKaU5ZdJNho4rLVmvPCLRo0apX/84x9q2bKl3TEBAAAAAMCfQCmFkFFeXq4nnn1QS3d8ImVXyWq2565Rnl0uNYhpr9Wr31ObNm1sTgkAAAAAAA4HSinYrqKiQi+++KLm5LwmZ6sqWc1/K6OKXIrb2Vq33/CQ2rVrZ3NKAAAAAABwOFFKwTaVlZV66aWXNHHiRG3dulVD/9ZNchh5il3y5rfULX/5pzp36WJ3TAAAAAAAUAcopVDvqqqq9NyLT2ruure1+OvN2rp1uyRp9vRNOmPIsbr5yvvVtVs3m1MCAAAAAIC6RCmFeuP3+/XCv5/T7NWvy9miUqaV0ZFnNdavS7dryJAhuueee9SNMgoAAAAAgKhAKYU6FwgE9O/Jz+vr5f8nZwufTCujoCRPmUuuoiZauPAV9erVy+6YAAAAAACgHlFKoc4Eg0G98cYbem/2U4rp6JNp/VsZVe6UY3OmRo+coKP69bM7JgAAAAAAsIHT7gCIPJZl6c0331SXLl10ySWXqKy8VEGXkafCKc+axrr+2Gf1+jOfUUgBAAAAABDFOFMKh41lWXrzrSn64IfntHFNnlatWi9J+nrarxqaNEBXXfAPnXD7iTanBAAAAAAAoYBSCn+aMUZvT39b7333pJwty2TaWWrTLFHzvnDo2GOP1b333quBAwfaHRMAAAAAAIQQSin8YcYYvff+O3r7q8flbFUqq50lS5K7yqngpgb65NOPddqpg+RwOOyOCgAAAAAAQgylFGrNGKOZM2fqxbfvV1znElnt/1tGWRtSNOL0WzX4lrMoowAAAAAAwAFxo3PUmDFGX3zxhfr3768zzjhDmzduUSDGktvvlHNNsi5oN15vPP2tzhx8NoUUAAAAAAA4KM6UQo188eVneuW9iSoozNOPP66UJC34ZoOyW/XX+affqGE3X0ARBQAAAAAAaoxSCgf1zayv9dK0f8rRYqeCHSylBd1KnBmrFlltNGHCBA0ZMkROJyfcAQAAAACA2qGUwn59P3u2/vXWBCk7X4EOliTJFXTIyonXc0//SxePuJQyCgAAAAAA/GGUUtjLvHnz9Ojzd8vdfpv87X9fRsXplCOv1GU3XimXy2VzSgAAAAAAEO441QWSpEWLFumss87SUUcdpR++m69AvCVX0CGti9PA5Gs15bE5uuKyqymkAAAAAADAYcGZUlFu0cKFemLyXdpdlauPPlouSdq8rlDFP7TS2SdeqqseuU4ej8fmlAAAAAAAINJQSkWppT//rMf+PU7+xrnytwsqTlKrIzIUKPVq3LhxuvzyyxUTE2N3TAAAAAAAEKEopaLMiuXL9chL/1BV+kb52wYlSU7LIbPJq79ee4tuuGaMvF6vzSkBAAAAAECko5SKEmvWrNH9j9ylQLPV8rf5rYwyDplNMerVbIhufOAWxcbG2pwSAAAAAABEC0qpCLdu3Trdd999eu211xSb4NHZE9r/VkZ51L3JmRpz3+2Kj4+3OyYAAAAAAIgylFIRasOGDXrwqTu0U6s0/dWfJUnlJZXa8kVQpwwYopvvHauEhASbUwIAAAAAgGhFKRVhNm3apIlP3amixOUKtAzKIanfoDZa/VOhbr31Vo0ePVpJSUl2xwQAAAAAAFGOUipCbNmyRQ88cad2xS9VoHlAkuSQ5Njq0cn9B2nmm+OUkpJib0gAAAAAAIDfUEqFuW3btmniw/epIHGuAtn/LaOcWz1qGTdAt99xjxo0aGBrRgAAAAAAgP9FKRWCfD6fpk2bpqnfTtX2ptuVuTVTI44foWHDhlW/Q15eXp4eeughPffcc/L5fBrxQPc9ZdQ2j5p7+uv22+5VamqqvS8EAAAAAADgAJx2BygoKFDr1q01a9as6m2jR4+W1+tVYmJi9a8XX3zRvpD1aMo7HyqrQw9dddvtWtVwvSoaVWplw/W66rbbldWhh158dapuGXedRj9ymp57/hn5fD5J0i8fFalZ3rF66ubP9NC9z1BIAQAAAACAkGbrmVJz5szRqFGjlJOTs9f2BQsW6LnnntOVV15pUzJ7THnnQ9389lJ5T7hOKS0WKzlzlbKCldrS2ivn1UcrZWczfbtlsgKNt8sYoxMvbK+v31ir66+/XrfddpsaN25s90sAAAAAAACoEdvOlJo8ebJGjBihiRMn7rU9EAhoyZIlOvLII21KZg+fz6cbb75NrrhkuZI2K6bROqUHK9XEUa5041OjZj+rWeePFEjJlyS5891qk9FH69ev16OPPkohBQAAAAAAwoptpdSgQYO0bt06DR06dK/ty5Ytk8/n08MPP6wmTZqoffv2euihh2RZlk1J68e0adNUXFwkuSoVm71Msa5KNVGldnhc8nmrZFwBFcRYcpU30LK3AuqXPkpPPfiKmjRpYnd0AAAAAACAWrPt8r3MzMz9bi8sLNSJJ56oa6+9Vq+++qoWL16sIUOGyOFw6LbbbttnfWVlpSorK6sfFxcXS5IsywqrIuu9996Tw+lUbLMVcicUKyvgl8tl5JdTDhklB40KgnH6sbSFNq9co1mzZumvf/2r3bFRA5ZlyRgTVl+P2BdzjAzMMTIwx8jAHMMfM4wMzDEyMMfIEElzrOlrCLl33zvppJN00kknVT/u27evbrrpJr311lv7LaUmTpyoCRMm7LM9Pz+/+ibg4WD79u1ypUgxjXLlNUaNXJWqNE4lVjmV5LQUZ1kqlEsxjXLlSt6zPi8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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化插值效果\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(time_series['date'], time_series['temperature'], 'ko-', label='Original', markersize=8, linewidth=2)\n",
    "plt.plot(time_series['date'], time_series['temp_ffill'], 's--', label='Forward Fill', alpha=0.7, markersize=6)\n",
    "plt.plot(time_series['date'], time_series['temp_linear'], '^--', label='Linear Interpolation', alpha=0.7, markersize=6)\n",
    "plt.plot(time_series['date'], time_series['temp_poly'], 'd--', label='Polynomial Interpolation', alpha=0.7, markersize=6)\n",
    "plt.title('Comparison of Different Imputation Methods', fontsize=14, fontweight='bold')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Temperature')\n",
    "plt.legend()\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 分组填充\n",
    "\n",
    "**策略**: 按类别分组，用各组的统计值填充\n",
    "\n",
    "**优势**: 更符合实际情况，避免统一填充带来的偏差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 分组填充 ===\n",
      "\n",
      "按部门填充薪资 - 各部门均值:\n",
      "department\n",
      "人力资源部   16953.00\n",
      "市场部     21110.00\n",
      "技术部     29468.00\n",
      "财务部     25247.00\n",
      "销售部     25797.00\n",
      "Name: salary, dtype: float64\n",
      "\n",
      "填充示例（缺失的用部门均值）:\n",
      "   employee_id department   salary  salary_by_dept\n",
      "0        E0001        技术部 49020.00        49020.00\n",
      "1        E0002      人力资源部  7693.00         7693.00\n",
      "2        E0003        财务部 34467.00        34467.00\n",
      "3        E0004        技术部 34723.00        34723.00\n",
      "4        E0005        技术部  8627.00         8627.00\n",
      "5        E0006      人力资源部 30939.00        30939.00\n",
      "6        E0007        技术部      NaN        29467.67\n",
      "7        E0008        技术部 26834.00        26834.00\n",
      "8        E0009      人力资源部 23047.00        23047.00\n",
      "9        E0010      人力资源部      NaN        16953.14\n",
      "10       E0011        销售部 15230.00        15230.00\n",
      "11       E0012        财务部 20707.00        20707.00\n",
      "12       E0013        财务部 26976.00        26976.00\n",
      "13       E0014        技术部      NaN        29467.67\n",
      "14       E0015        财务部 28776.00        28776.00\n",
      "15       E0016        技术部      NaN        29467.67\n",
      "16       E0017        销售部  6306.00         6306.00\n",
      "17       E0018      人力资源部 11776.00        11776.00\n",
      "18       E0019      人力资源部      NaN        16953.14\n",
      "19       E0020      人力资源部 14474.00        14474.00\n"
     ]
    }
   ],
   "source": [
    "# 分组填充示例\n",
    "df_group = df.copy()\n",
    "\n",
    "print(\"=== 分组填充 ===\")\n",
    "\n",
    "# 按部门填充薪资（用各部门的平均薪资）\n",
    "df_group['salary_by_dept'] = df_group.groupby('department')['salary'].transform(\n",
    "    lambda x: x.fillna(x.mean())\n",
    ")\n",
    "\n",
    "print(\"\\n按部门填充薪资 - 各部门均值:\")\n",
    "dept_salary = df_group.groupby('department')['salary'].mean()\n",
    "print(dept_salary.round(0))\n",
    "\n",
    "print(\"\\n填充示例（缺失的用部门均值）:\")\n",
    "sample = df_group[['employee_id', 'department', 'salary', 'salary_by_dept']].head(20)\n",
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对比全局填充vs分组填充\n",
    "df_group['salary_global'] = df_group['salary'].fillna(df_group['salary'].mean())\n",
    "\n",
    "print(\"\\n=== 全局填充 vs 分组填充对比 ===\")\n",
    "comparison = df_group.groupby('department').agg({\n",
    "    'salary': 'mean',\n",
    "    'salary_global': 'mean',\n",
    "    'salary_by_dept': 'mean'\n",
    "}).round(0)\n",
    "comparison.columns = ['原始均值', '全局填充后', '分组填充后']\n",
    "print(comparison)\n",
    "\n",
    "print(\"\\n结论: 分组填充保持了各组的特征，全局填充会拉平差异\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7 缺失值处理决策树\n",
    "\n",
    "```\n",
    "开始\n",
    "  ↓\n",
    "缺失比例 > 50%?\n",
    "  ├─ 是 → 考虑删除该列\n",
    "  └─ 否 ↓\n",
    "       数据量充足 且 缺失<5%?\n",
    "         ├─ 是 → 删除缺失行\n",
    "         └─ 否 ↓\n",
    "              是否分类变量?\n",
    "                ├─ 是 → 众数填充 或 \"缺失\"类别\n",
    "                └─ 否 ↓\n",
    "                     是否时间序列?\n",
    "                       ├─ 是 → 插值法 或 前向填充\n",
    "                       └─ 否 ↓\n",
    "                            有极端值?\n",
    "                              ├─ 是 → 中位数填充\n",
    "                              └─ 否 → 均值填充\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 四、异常值检测\n",
    "\n",
    "### 4.1 什么是异常值？\n",
    "\n",
    "**定义**: 与数据集中大多数观测值显著不同的值\n",
    "\n",
    "**类型**:\n",
    "1. **点异常**: 单个数据点异常（如年龄-5岁）\n",
    "2. **上下文异常**: 在特定上下文下异常（如夏天温度-10°C）\n",
    "3. **集体异常**: 一组数据整体异常\n",
    "\n",
    "**产生原因**:\n",
    "- 数据录入错误\n",
    "- 测量误差\n",
    "- 真实的极端值\n",
    "- 数据处理错误"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 创建了包含异常值的数据集\n",
      "数据规模: (100, 4)\n",
      "\n",
      "数据样本:\n",
      "  employee_id   salary  age  experience_years\n",
      "0       E0001 22483.57   34                15\n",
      "1       E0002 19308.68   24                28\n",
      "2       E0003 23238.44   27                 8\n",
      "3       E0004 27615.15   29                 3\n",
      "4       E0005 18829.23   48                 0\n",
      "5       E0006 18829.32   -5                 3\n",
      "6       E0007 27896.06   58                 0\n",
      "7       E0008 23837.17   54                13\n",
      "8       E0009 17652.63   45                20\n",
      "9       E0010 22712.80   36                15\n"
     ]
    }
   ],
   "source": [
    "# 创建包含异常值的数据集\n",
    "np.random.seed(42)\n",
    "n = 100\n",
    "\n",
    "# 正常数据\n",
    "normal_salary = np.random.normal(20000, 5000, 95)\n",
    "\n",
    "# 异常值\n",
    "outliers = np.array([-5000, 150000, 200000, 250000, 999999])\n",
    "\n",
    "# 合并\n",
    "all_salary = np.concatenate([normal_salary, outliers])\n",
    "\n",
    "df_outlier = pd.DataFrame({\n",
    "    'employee_id': [f'E{str(i).zfill(4)}' for i in range(1, n+1)],\n",
    "    'salary': all_salary,\n",
    "    'age': np.random.randint(22, 60, n),\n",
    "    'experience_years': np.random.randint(0, 30, n)\n",
    "})\n",
    "\n",
    "# 添加一些年龄异常值\n",
    "df_outlier.loc[[5, 15, 25], 'age'] = [-5, 150, 200]\n",
    "\n",
    "print(\"✅ 创建了包含异常值的数据集\")\n",
    "print(f\"数据规模: {df_outlier.shape}\")\n",
    "print(\"\\n数据样本:\")\n",
    "print(df_outlier.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 方法1: 业务规则法\n",
    "\n",
    "**最直接有效的方法！**\n",
    "\n",
    "**原理**: 基于业务知识设定合理范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 业务规则检测\n",
    "print(\"=== 业务规则法检测异常值 ===\")\n",
    "\n",
    "# 规则1: 年龄应在18-65之间\n",
    "age_outliers = df_outlier[(df_outlier['age'] < 18) | (df_outlier['age'] > 65)]\n",
    "print(f\"\\n1. 年龄异常（<18 或 >65）: {len(age_outliers)}个\")\n",
    "print(age_outliers[['employee_id', 'age']])\n",
    "\n",
    "# 规则2: 薪资应在3000-100000之间\n",
    "salary_outliers = df_outlier[(df_outlier['salary'] < 3000) | (df_outlier['salary'] > 100000)]\n",
    "print(f\"\\n2. 薪资异常（<3000 或 >100000）: {len(salary_outliers)}个\")\n",
    "print(salary_outliers[['employee_id', 'salary']].sort_values('salary'))\n",
    "\n",
    "# 规则3: 工作年限不能超过年龄-18\n",
    "exp_outliers = df_outlier[df_outlier['experience_years'] > (df_outlier['age'] - 18)]\n",
    "print(f\"\\n3. 工作年限异常（超过年龄-18）: {len(exp_outliers)}个\")\n",
    "print(exp_outliers[['employee_id', 'age', 'experience_years']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 方法2: 统计法 - 3σ原则\n",
    "\n",
    "**原理**: 假设数据服从正态分布\n",
    "- 99.7%的数据在 μ±3σ 范围内\n",
    "- 超出此范围的视为异常\n",
    "\n",
    "**公式**:\n",
    "- 下界: mean - 3 × std\n",
    "- 上界: mean + 3 × std\n",
    "\n",
    "**适用**: 数据近似正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== salary 的3σ检测 ===\n",
      "均值: 34487.55\n",
      "标准差: 102703.04\n",
      "正常范围: [-273621.58, 342596.68]\n",
      "异常值数量: 1\n",
      "\n",
      "异常值:\n",
      "   employee_id    salary\n",
      "99       E0100 999999.00\n"
     ]
    }
   ],
   "source": [
    "# 3σ原则检测\n",
    "def detect_outliers_3sigma(data, column):\n",
    "    \"\"\"\n",
    "    使用3σ原则检测异常值\n",
    "    \"\"\"\n",
    "    mean = data[column].mean()\n",
    "    std = data[column].std()\n",
    "    lower_bound = mean - 3 * std\n",
    "    upper_bound = mean + 3 * std\n",
    "    \n",
    "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)]\n",
    "    \n",
    "    print(f\"\\n=== {column} 的3σ检测 ===\")\n",
    "    print(f\"均值: {mean:.2f}\")\n",
    "    print(f\"标准差: {std:.2f}\")\n",
    "    print(f\"正常范围: [{lower_bound:.2f}, {upper_bound:.2f}]\")\n",
    "    print(f\"异常值数量: {len(outliers)}\")\n",
    "    \n",
    "    return outliers, lower_bound, upper_bound\n",
    "\n",
    "# 检测薪资异常\n",
    "salary_outliers_3sigma, lower, upper = detect_outliers_3sigma(df_outlier, 'salary')\n",
    "print(\"\\n异常值:\")\n",
    "print(salary_outliers_3sigma[['employee_id', 'salary']].sort_values('salary'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 方法3: IQR法（四分位距法）⭐推荐\n",
    "\n",
    "**原理**: 基于数据的分位数，不假设分布\n",
    "\n",
    "**步骤**:\n",
    "1. 计算Q1（25%分位数）和Q3（75%分位数）\n",
    "2. 计算IQR = Q3 - Q1\n",
    "3. 下界 = Q1 - 1.5 × IQR\n",
    "4. 上界 = Q3 + 1.5 × IQR\n",
    "5. 超出界限的为异常值\n",
    "\n",
    "**优点**: \n",
    "- 不受极端值影响\n",
    "- 不假设数据分布\n",
    "- 箱线图的理论基础\n",
    "\n",
    "**Excel对比**: Excel可以用箱线图，但需要手动计算边界，Pandas自动化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== salary 的IQR检测 ===\n",
      "Q1 (25%分位数): 16995.47\n",
      "Q3 (75%分位数): 23103.40\n",
      "IQR: 6107.93\n",
      "正常范围: [7833.58, 32265.28]\n",
      "异常值数量: 6 (6.0%)\n",
      "\n",
      "异常值详情:\n",
      "   employee_id    salary\n",
      "95       E0096  -5000.00\n",
      "74       E0075   6901.27\n",
      "96       E0097 150000.00\n",
      "97       E0098 200000.00\n",
      "98       E0099 250000.00\n",
      "99       E0100 999999.00\n"
     ]
    }
   ],
   "source": [
    "# IQR法检测\n",
    "def detect_outliers_iqr(data, column):\n",
    "    \"\"\"\n",
    "    使用IQR方法检测异常值\n",
    "    \"\"\"\n",
    "    Q1 = data[column].quantile(0.25)\n",
    "    Q3 = data[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    \n",
    "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)]\n",
    "    \n",
    "    print(f\"\\n=== {column} 的IQR检测 ===\")\n",
    "    print(f\"Q1 (25%分位数): {Q1:.2f}\")\n",
    "    print(f\"Q3 (75%分位数): {Q3:.2f}\")\n",
    "    print(f\"IQR: {IQR:.2f}\")\n",
    "    print(f\"正常范围: [{lower_bound:.2f}, {upper_bound:.2f}]\")\n",
    "    print(f\"异常值数量: {len(outliers)} ({len(outliers)/len(data)*100:.1f}%)\")\n",
    "    \n",
    "    return outliers, lower_bound, upper_bound\n",
    "\n",
    "# 检测薪资异常\n",
    "salary_outliers_iqr, iqr_lower, iqr_upper = detect_outliers_iqr(df_outlier, 'salary')\n",
    "print(\"\\n异常值详情:\")\n",
    "print(salary_outliers_iqr[['employee_id', 'salary']].sort_values('salary'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 方法4: Z-Score法\n",
    "\n",
    "**原理**: 标准化后的值，表示距离均值多少个标准差\n",
    "\n",
    "**公式**: Z = (X - μ) / σ\n",
    "\n",
    "**判断**: |Z| > 3 为异常值（也可以用2.5或3.5）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== salary 的Z-Score检测 ===\n",
      "阈值: |Z| > 3\n",
      "异常值数量: 1\n",
      "\n",
      "异常值详情:\n",
      "   employee_id    salary  z_score\n",
      "99       E0100 999999.00     9.40\n"
     ]
    }
   ],
   "source": [
    "# Z-Score法检测\n",
    "def detect_outliers_zscore(data, column, threshold=3):\n",
    "    \"\"\"\n",
    "    使用Z-Score方法检测异常值\n",
    "    \"\"\"\n",
    "    mean = data[column].mean()\n",
    "    std = data[column].std()\n",
    "    \n",
    "    # 计算Z-Score\n",
    "    data['z_score'] = np.abs((data[column] - mean) / std)\n",
    "    \n",
    "    outliers = data[data['z_score'] > threshold]\n",
    "    \n",
    "    print(f\"\\n=== {column} 的Z-Score检测 ===\")\n",
    "    print(f\"阈值: |Z| > {threshold}\")\n",
    "    print(f\"异常值数量: {len(outliers)}\")\n",
    "    \n",
    "    return outliers\n",
    "\n",
    "# 检测薪资异常\n",
    "salary_outliers_z = detect_outliers_zscore(df_outlier.copy(), 'salary')\n",
    "print(\"\\n异常值详情:\")\n",
    "print(salary_outliers_z[['employee_id', 'salary', 'z_score']].sort_values('z_score', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 异常值可视化\n",
    "\n",
    "**箱线图（Boxplot）**: 最直观的异常值可视化方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "—— 阈值与统计 ——\n",
      "业务规则: < 1000 或 > 50000 -> 5 个\n",
      "3σ原则: [-273621.58, 342596.68] -> 1 个\n",
      "IQR法:   [7833.58, 32265.28] -> 6 个\n",
      "Z-Score: |z| > 3 -> 1 个\n"
     ]
    }
   ],
   "source": [
    "# ======================\n",
    "# 完整可运行版本（四图 + 四种方法对比）\n",
    "# ======================\n",
    "\n",
    "# 1) 依赖导入\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "# 2) 准备数据（假设已有 df_outlier 且包含 'salary' 列）\n",
    "# 如果你还没有 df_outlier，可以用下面三行生成一个示例数据：\n",
    "# np.random.seed(42)\n",
    "# df_outlier = pd.DataFrame({'salary': np.concatenate([np.random.normal(12000, 2000, 500), [800, 90000, 95000]])})\n",
    "# df_outlier = df_outlier.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "if 'df_outlier' not in globals():\n",
    "    raise ValueError(\"当前环境未找到变量 df_outlier。请先准备 DataFrame，如 df_outlier[['salary']]。\")\n",
    "\n",
    "if 'salary' not in df_outlier.columns:\n",
    "    raise ValueError(\"df_outlier 中未找到 'salary' 列，请检查列名或替换为你的列名。\")\n",
    "\n",
    "# 丢弃 salary 中的缺失值，保留干净视图用于计算与绘图\n",
    "df_outlier = df_outlier.copy()\n",
    "df_outlier = df_outlier[~df_outlier['salary'].isna()].reset_index(drop=True)\n",
    "\n",
    "# 3) 四种异常值检测（业务规则、3σ、IQR、Z-Score）\n",
    "# 3.1 业务规则（示例阈值：<1000 或 >50000，可按需要调整）\n",
    "lower_rule, upper_rule = 1000, 50000\n",
    "salary_outliers = df_outlier[(df_outlier['salary'] < lower_rule) | (df_outlier['salary'] > upper_rule)]\n",
    "\n",
    "# 3.2 3σ原则\n",
    "mean_salary = df_outlier['salary'].mean()\n",
    "std_salary = df_outlier['salary'].std(ddof=1)  # 样本标准差\n",
    "lower_3sigma = mean_salary - 3 * std_salary\n",
    "upper_3sigma = mean_salary + 3 * std_salary\n",
    "salary_outliers_3sigma = df_outlier[(df_outlier['salary'] < lower_3sigma) | (df_outlier['salary'] > upper_3sigma)]\n",
    "\n",
    "# 3.3 IQR 法\n",
    "Q1 = df_outlier['salary'].quantile(0.25)\n",
    "Q3 = df_outlier['salary'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "iqr_lower = Q1 - 1.5 * IQR\n",
    "iqr_upper = Q3 + 1.5 * IQR\n",
    "salary_outliers_iqr = df_outlier[(df_outlier['salary'] < iqr_lower) | (df_outlier['salary'] > iqr_upper)]\n",
    "\n",
    "# 3.4 Z-Score 法（绝对 z 分数 > 3）\n",
    "z_scores = stats.zscore(df_outlier['salary'], nan_policy='omit')\n",
    "salary_outliers_z = df_outlier[np.abs(z_scores) > 3]\n",
    "\n",
    "# 4) 可视化（四图合一）\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "# 4.1 箱线图（IQR 边界线）\n",
    "axes[0, 0].boxplot(df_outlier['salary'], vert=True)\n",
    "axes[0, 0].set_title('Salary Boxplot (IQR Method)', fontsize=14, fontweight='bold')\n",
    "axes[0, 0].set_ylabel('Salary')\n",
    "axes[0, 0].axhline(y=iqr_lower, color='r', linestyle='--', label=f'Lower Bound: {iqr_lower:.0f}')\n",
    "axes[0, 0].axhline(y=iqr_upper, color='r', linestyle='--', label=f'Upper Bound: {iqr_upper:.0f}')\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(alpha=0.3)\n",
    "\n",
    "# 4.2 直方图（IQR 边界线）\n",
    "axes[0, 1].hist(df_outlier['salary'], bins=30, edgecolor='black', alpha=0.7, color='skyblue')\n",
    "axes[0, 1].axvline(x=iqr_lower, color='r', linestyle='--', linewidth=2, label='IQR Bounds')\n",
    "axes[0, 1].axvline(x=iqr_upper, color='r', linestyle='--', linewidth=2)\n",
    "axes[0, 1].set_title('Salary Distribution with IQR Bounds', fontsize=14, fontweight='bold')\n",
    "axes[0, 1].set_xlabel('Salary')\n",
    "axes[0, 1].set_ylabel('Frequency')\n",
    "axes[0, 1].legend()\n",
    "axes[0, 1].grid(alpha=0.3)\n",
    "\n",
    "# 4.3 散点图（标注 IQR 异常点）\n",
    "axes[1, 0].scatter(df_outlier.index, df_outlier['salary'], alpha=0.6, s=50)\n",
    "axes[1, 0].axhline(y=iqr_lower, color='r', linestyle='--', linewidth=2, label='IQR Bounds')\n",
    "axes[1, 0].axhline(y=iqr_upper, color='r', linestyle='--', linewidth=2)\n",
    "\n",
    "outlier_indices = salary_outliers_iqr.index\n",
    "axes[1, 0].scatter(\n",
    "    outlier_indices,\n",
    "    df_outlier.loc[outlier_indices, 'salary'],\n",
    "    color='red', s=100, marker='x', linewidths=3, label='Outliers'\n",
    ")\n",
    "axes[1, 0].set_title('Salary Scatter Plot with Outliers', fontsize=14, fontweight='bold')\n",
    "axes[1, 0].set_xlabel('Index')\n",
    "axes[1, 0].set_ylabel('Salary')\n",
    "axes[1, 0].legend()\n",
    "axes[1, 0].grid(alpha=0.3)\n",
    "\n",
    "# 4.4 四种方法异常数对比\n",
    "methods_comparison = pd.DataFrame({\n",
    "    '方法': ['业务规则', '3σ原则', 'IQR法', 'Z-Score'],\n",
    "    '异常值数量': [\n",
    "        len(salary_outliers),\n",
    "        len(salary_outliers_3sigma),\n",
    "        len(salary_outliers_iqr),\n",
    "        len(salary_outliers_z)\n",
    "    ]\n",
    "})\n",
    "axes[1, 1].bar(\n",
    "    methods_comparison['方法'],\n",
    "    methods_comparison['异常值数量'],\n",
    "    color=['#ff9999', '#66b3ff', '#99ff99', '#ffcc99'],\n",
    "    edgecolor='black'\n",
    ")\n",
    "axes[1, 1].set_title('Outlier Detection Methods Comparison', fontsize=14, fontweight='bold')\n",
    "axes[1, 1].set_ylabel('Number of Outliers')\n",
    "axes[1, 1].grid(axis='y', alpha=0.3)\n",
    "for i, v in enumerate(methods_comparison['异常值数量']):\n",
    "    axes[1, 1].text(i, v + max(methods_comparison['异常值数量']) * 0.02 + 0.01, str(v),\n",
    "                    ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 5) 可选：把四种方法的阈值与数量打印出来便于汇报\n",
    "print(\"—— 阈值与统计 ——\")\n",
    "print(f\"业务规则: < {lower_rule:.0f} 或 > {upper_rule:.0f} -> {len(salary_outliers)} 个\")\n",
    "print(f\"3σ原则: [{lower_3sigma:.2f}, {upper_3sigma:.2f}] -> {len(salary_outliers_3sigma)} 个\")\n",
    "print(f\"IQR法:   [{iqr_lower:.2f}, {iqr_upper:.2f}] -> {len(salary_outliers_iqr)} 个\")\n",
    "print(f\"Z-Score: |z| > 3 -> {len(salary_outliers_z)} 个\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 五、异常值处理策略\n",
    "\n",
    "### 5.1 策略1: 删除\n",
    "\n",
    "**适用**: 确认是错误数据，且数量不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 删除异常值 ===\n",
      "原始数据: 100行\n",
      "删除异常值后: 94行\n",
      "删除了: 6行 (6.0%)\n",
      "\n",
      "删除后的统计:\n",
      "count      94.00\n",
      "mean    19647.39\n",
      "std      4431.29\n",
      "min     10062.16\n",
      "25%     17044.75\n",
      "50%     19365.22\n",
      "75%     22545.65\n",
      "max     29261.39\n",
      "Name: salary, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 删除异常值\n",
    "df_remove = df_outlier.copy()\n",
    "\n",
    "print(\"=== 删除异常值 ===\")\n",
    "print(f\"原始数据: {len(df_remove)}行\")\n",
    "\n",
    "# 使用IQR法识别的异常值\n",
    "Q1 = df_remove['salary'].quantile(0.25)\n",
    "Q3 = df_remove['salary'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "lower = Q1 - 1.5 * IQR\n",
    "upper = Q3 + 1.5 * IQR\n",
    "\n",
    "# 删除异常值\n",
    "df_remove = df_remove[(df_remove['salary'] >= lower) & (df_remove['salary'] <= upper)]\n",
    "print(f\"删除异常值后: {len(df_remove)}行\")\n",
    "print(f\"删除了: {len(df_outlier) - len(df_remove)}行 ({(len(df_outlier)-len(df_remove))/len(df_outlier)*100:.1f}%)\")\n",
    "\n",
    "print(\"\\n删除后的统计:\")\n",
    "print(df_remove['salary'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 策略2: 替换为边界值（Capping/Flooring）\n",
    "\n",
    "**适用**: 想保留数据量，但限制极端值影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 替换为边界值 ===\n",
      "\n",
      "替换前 - 薪资范围: [-5000, 999999]\n",
      "替换后 - 薪资范围: [7834, 32265]\n",
      "\n",
      "被替换的异常值数量: 6\n",
      "\n",
      "被替换的记录:\n",
      "   employee_id    salary  salary_capped\n",
      "95       E0096  -5000.00        7833.58\n",
      "74       E0075   6901.27        7833.58\n",
      "96       E0097 150000.00       32265.28\n",
      "97       E0098 200000.00       32265.28\n",
      "98       E0099 250000.00       32265.28\n",
      "99       E0100 999999.00       32265.28\n"
     ]
    }
   ],
   "source": [
    "# 替换为边界值\n",
    "df_cap = df_outlier.copy()\n",
    "\n",
    "print(\"=== 替换为边界值 ===\")\n",
    "print(f\"\\n替换前 - 薪资范围: [{df_cap['salary'].min():.0f}, {df_cap['salary'].max():.0f}]\")\n",
    "\n",
    "# 方法1: 使用IQR边界\n",
    "df_cap['salary_capped'] = df_cap['salary'].clip(lower=lower, upper=upper)\n",
    "\n",
    "print(f\"替换后 - 薪资范围: [{df_cap['salary_capped'].min():.0f}, {df_cap['salary_capped'].max():.0f}]\")\n",
    "print(f\"\\n被替换的异常值数量: {(df_cap['salary'] != df_cap['salary_capped']).sum()}\")\n",
    "\n",
    "# 查看被替换的数据\n",
    "print(\"\\n被替换的记录:\")\n",
    "changed = df_cap[df_cap['salary'] != df_cap['salary_capped']][['employee_id', 'salary', 'salary_capped']]\n",
    "print(changed.sort_values('salary'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 策略3: 替换为统计值\n",
    "\n",
    "**适用**: 异常值较少，用中位数或均值替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 替换为统计值 ===\n",
      "中位数: 19365\n",
      "替换了 6 个异常值\n",
      "\n",
      "替换前后对比:\n",
      "    指标      原始数据      替换后\n",
      "0   均值  34487.55 19630.46\n",
      "1  中位数  19530.85 19365.22\n",
      "2  标准差 102703.04  4295.44\n",
      "3  最小值  -5000.00 10062.16\n",
      "4  最大值 999999.00 29261.39\n"
     ]
    }
   ],
   "source": [
    "# 替换为统计值\n",
    "df_replace = df_outlier.copy()\n",
    "\n",
    "print(\"=== 替换为统计值 ===\")\n",
    "\n",
    "# 识别异常值\n",
    "is_outlier = (df_replace['salary'] < lower) | (df_replace['salary'] > upper)\n",
    "\n",
    "# 计算非异常值的中位数\n",
    "median_salary = df_replace[~is_outlier]['salary'].median()\n",
    "\n",
    "# 替换异常值为中位数\n",
    "df_replace['salary_replaced'] = df_replace['salary'].copy()\n",
    "df_replace.loc[is_outlier, 'salary_replaced'] = median_salary\n",
    "\n",
    "print(f\"中位数: {median_salary:.0f}\")\n",
    "print(f\"替换了 {is_outlier.sum()} 个异常值\")\n",
    "\n",
    "print(\"\\n替换前后对比:\")\n",
    "comparison = pd.DataFrame({\n",
    "    '指标': ['均值', '中位数', '标准差', '最小值', '最大值'],\n",
    "    '原始数据': [\n",
    "        df_replace['salary'].mean(),\n",
    "        df_replace['salary'].median(),\n",
    "        df_replace['salary'].std(),\n",
    "        df_replace['salary'].min(),\n",
    "        df_replace['salary'].max()\n",
    "    ],\n",
    "    '替换后': [\n",
    "        df_replace['salary_replaced'].mean(),\n",
    "        df_replace['salary_replaced'].median(),\n",
    "        df_replace['salary_replaced'].std(),\n",
    "        df_replace['salary_replaced'].min(),\n",
    "        df_replace['salary_replaced'].max()\n",
    "    ]\n",
    "})\n",
    "print(comparison.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 策略4: 保留并标记\n",
    "\n",
    "**适用**: 不确定是否应该删除，保留但做标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 保留并标记异常值 ===\n",
      "\n",
      "异常值统计:\n",
      "outlier_type\n",
      "normal      94\n",
      "too_high     4\n",
      "too_low      2\n",
      "Name: count, dtype: int64\n",
      "\n",
      "标记为异常的记录:\n",
      "   employee_id    salary outlier_type\n",
      "95       E0096  -5000.00      too_low\n",
      "74       E0075   6901.27      too_low\n",
      "96       E0097 150000.00     too_high\n",
      "97       E0098 200000.00     too_high\n",
      "98       E0099 250000.00     too_high\n",
      "99       E0100 999999.00     too_high\n"
     ]
    }
   ],
   "source": [
    "# 保留并标记\n",
    "df_flag = df_outlier.copy()\n",
    "\n",
    "print(\"=== 保留并标记异常值 ===\")\n",
    "\n",
    "# 添加标记列\n",
    "df_flag['is_outlier'] = ((df_flag['salary'] < lower) | (df_flag['salary'] > upper))\n",
    "df_flag['outlier_type'] = 'normal'\n",
    "df_flag.loc[df_flag['salary'] < lower, 'outlier_type'] = 'too_low'\n",
    "df_flag.loc[df_flag['salary'] > upper, 'outlier_type'] = 'too_high'\n",
    "\n",
    "print(\"\\n异常值统计:\")\n",
    "print(df_flag['outlier_type'].value_counts())\n",
    "\n",
    "print(\"\\n标记为异常的记录:\")\n",
    "print(df_flag[df_flag['is_outlier']][['employee_id', 'salary', 'outlier_type']].sort_values('salary'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 不同策略效果对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化不同处理策略的效果\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
    "\n",
    "# 1. 原始数据\n",
    "axes[0, 0].hist(df_outlier['salary'], bins=30, edgecolor='black', alpha=0.7, color='lightblue')\n",
    "axes[0, 0].set_title('Original Data', fontsize=12, fontweight='bold')\n",
    "axes[0, 0].set_xlabel('Salary')\n",
    "axes[0, 0].set_ylabel('Frequency')\n",
    "axes[0, 0].grid(alpha=0.3)\n",
    "\n",
    "# 2. 删除异常值后\n",
    "axes[0, 1].hist(df_remove['salary'], bins=30, edgecolor='black', alpha=0.7, color='lightcoral')\n",
    "axes[0, 1].set_title('After Removal', fontsize=12, fontweight='bold')\n",
    "axes[0, 1].set_xlabel('Salary')\n",
    "axes[0, 1].grid(alpha=0.3)\n",
    "\n",
    "# 3. 替换为边界值后\n",
    "axes[0, 2].hist(df_cap['salary_capped'], bins=30, edgecolor='black', alpha=0.7, color='lightgreen')\n",
    "axes[0, 2].set_title('After Capping', fontsize=12, fontweight='bold')\n",
    "axes[0, 2].set_xlabel('Salary')\n",
    "axes[0, 2].grid(alpha=0.3)\n",
    "\n",
    "# 4. 替换为中位数后\n",
    "axes[1, 0].hist(df_replace['salary_replaced'], bins=30, edgecolor='black', alpha=0.7, color='lightyellow')\n",
    "axes[1, 0].set_title('After Median Replacement', fontsize=12, fontweight='bold')\n",
    "axes[1, 0].set_xlabel('Salary')\n",
    "axes[1, 0].set_ylabel('Frequency')\n",
    "axes[1, 0].grid(alpha=0.3)\n",
    "\n",
    "# 5. 箱线图对比\n",
    "data_to_plot = [\n",
    "    df_outlier['salary'],\n",
    "    df_remove['salary'],\n",
    "    df_cap['salary_capped'],\n",
    "    df_replace['salary_replaced']\n",
    "]\n",
    "axes[1, 1].boxplot(data_to_plot, labels=['Original', 'Removed', 'Capped', 'Replaced'])\n",
    "axes[1, 1].set_title('Boxplot Comparison', fontsize=12, fontweight='bold')\n",
    "axes[1, 1].set_ylabel('Salary')\n",
    "axes[1, 1].grid(alpha=0.3)\n",
    "\n",
    "# 6. 统计对比表\n",
    "stats_comparison = pd.DataFrame({\n",
    "    'Original': df_outlier['salary'].describe(),\n",
    "    'Removed': df_remove['salary'].describe(),\n",
    "    'Capped': df_cap['salary_capped'].describe(),\n",
    "    'Replaced': df_replace['salary_replaced'].describe()\n",
    "}).T[['mean', 'std', 'min', 'max']].round(0)\n",
    "\n",
    "axes[1, 2].axis('tight')\n",
    "axes[1, 2].axis('off')\n",
    "table = axes[1, 2].table(cellText=stats_comparison.values,\n",
    "                        rowLabels=stats_comparison.index,\n",
    "                        colLabels=stats_comparison.columns,\n",
    "                        cellLoc='center',\n",
    "                        loc='center')\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(10)\n",
    "table.scale(1, 2)\n",
    "axes[1, 2].set_title('Statistics Comparison', fontsize=12, fontweight='bold', pad=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 六、完整清洗实战案例\n",
    "\n",
    "### 案例：清洗真实的员工数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 创建了真实场景的脏数据\n",
      "数据规模: (103, 7)\n",
      "\n",
      "数据预览:\n",
      "   employee_id  name  age department salary  hire_date              email\n",
      "0        E0001   员工1   50        市场部  36065 2020-01-01   emp1@company.com\n",
      "1        E0002   员工2   36        技术部  30199 2020-01-04   emp2@company.com\n",
      "2        E0003   员工3   29        销售部  46976 2020-01-07   emp3@company.com\n",
      "3        E0004   员工4   42        技术部  21371 2020-01-10   emp4@company.com\n",
      "4        E0005   员工5   40        技术部  16835 2020-01-13   emp5@company.com\n",
      "5        E0006   员工6   44       None   7049 2020-01-16   emp6@company.com\n",
      "6        E0007   员工7   32       人力资源  36616 2020-01-19   emp7@company.com\n",
      "7        E0008   员工8   32        市场部  43191 2020-01-22   emp8@company.com\n",
      "8        E0009   员工9   45        销售部  25932 2020-01-25   emp9@company.com\n",
      "9         None  员工10   57        销售部  34855 2020-01-28  emp10@company.com\n",
      "10       E0011  员工11   45        销售部  12158 2020-01-31  emp11@company.com\n",
      "11       E0012  员工12   24        市场部  48016 2020-02-03  emp12@company.com\n",
      "12       E0013  员工13   43        销售部  12400 2020-02-06  emp13@company.com\n",
      "13       E0014  员工14   23        技术部  47642 2020-02-09  emp14@company.com\n",
      "14       E0015  None   45        销售部  20151 2020-02-12  emp15@company.com\n"
     ]
    }
   ],
   "source": [
    "# 创建模拟真实场景的脏数据\n",
    "np.random.seed(42)\n",
    "n = 100\n",
    "\n",
    "messy_data = {\n",
    "    'employee_id': [f'E{str(i).zfill(4)}' if i % 10 != 0 else None for i in range(1, n+1)],\n",
    "    'name': [f'员工{i}' if i % 15 != 0 else None for i in range(1, n+1)],\n",
    "    'age': np.concatenate([np.random.randint(22, 60, 95), [-5, 150, 200, 0, 999]]),\n",
    "    'department': np.random.choice(['销售部', '技术部', '市场部', '人力资源', None], n, p=[0.3, 0.3, 0.2, 0.15, 0.05]),\n",
    "    'salary': np.concatenate([np.random.randint(5000, 50000, 90), [-1000, 0, 200000, 500000, 999999] + [None]*5]),\n",
    "    'hire_date': pd.date_range('2020-01-01', periods=n, freq='3D'),\n",
    "    'email': [f'emp{i}@company.com' if i % 20 != 0 else None for i in range(1, n+1)],\n",
    "}\n",
    "\n",
    "df_messy = pd.DataFrame(messy_data)\n",
    "\n",
    "# 添加重复行\n",
    "df_messy = pd.concat([df_messy, df_messy.iloc[[10, 11, 12]]], ignore_index=True)\n",
    "\n",
    "print(\"✅ 创建了真实场景的脏数据\")\n",
    "print(f\"数据规模: {df_messy.shape}\")\n",
    "print(\"\\n数据预览:\")\n",
    "print(df_messy.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "步骤1: 数据质量检查\n",
      "======================================================================\n",
      "\n",
      "1.1 缺失值检查:\n",
      "employee_id    10\n",
      "name            6\n",
      "age             0\n",
      "department      4\n",
      "salary          5\n",
      "hire_date       0\n",
      "email           5\n",
      "dtype: int64\n",
      "\n",
      "1.2 重复值检查:\n",
      "重复行数: 3\n",
      "\n",
      "1.3 数据类型:\n",
      "employee_id            object\n",
      "name                   object\n",
      "age                     int64\n",
      "department             object\n",
      "salary                 object\n",
      "hire_date      datetime64[ns]\n",
      "email                  object\n",
      "dtype: object\n",
      "\n",
      "1.4 描述性统计:\n",
      "         age\n",
      "count 103.00\n",
      "mean   50.50\n",
      "std    97.03\n",
      "min    -5.00\n",
      "25%    29.50\n",
      "50%    40.00\n",
      "75%    47.00\n",
      "max   999.00\n"
     ]
    }
   ],
   "source": [
    "# 步骤1: 数据质量检查\n",
    "print(\"=\"*70)\n",
    "print(\"步骤1: 数据质量检查\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "print(\"\\n1.1 缺失值检查:\")\n",
    "print(df_messy.isnull().sum())\n",
    "\n",
    "print(\"\\n1.2 重复值检查:\")\n",
    "print(f\"重复行数: {df_messy.duplicated().sum()}\")\n",
    "\n",
    "print(\"\\n1.3 数据类型:\")\n",
    "print(df_messy.dtypes)\n",
    "\n",
    "print(\"\\n1.4 描述性统计:\")\n",
    "print(df_messy[['age', 'salary']].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "步骤2: 数据清洗\n",
      "======================================================================\n",
      "\n",
      "清洗前: (103, 7)\n",
      "2.1 去重后: (100, 7)\n",
      "2.2 删除ID缺失后: (90, 7)\n",
      "\n",
      "2.3 年龄异常值: 4个\n",
      "     删除后: (86, 7)\n",
      "\n",
      "2.4 薪资统计:\n",
      "     最小值: -1000\n",
      "     最大值: 999999\n",
      "     异常值数量: 5\n",
      "     删除后: (81, 7)\n",
      "\n",
      "2.5 填充缺失值:\n",
      "     name缺失: 0\n",
      "     department缺失: 0\n",
      "     salary缺失: 0\n",
      "     email缺失: 0 (保留)\n",
      "\n",
      "✅ 清洗完成: (81, 7)\n"
     ]
    }
   ],
   "source": [
    "# 步骤2: 开始清洗\n",
    "df_clean = df_messy.copy()\n",
    "\n",
    "print(\"=\"*70)\n",
    "print(\"步骤2: 数据清洗\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "print(f\"\\n清洗前: {df_clean.shape}\")\n",
    "\n",
    "# 2.1 删除完全重复的行\n",
    "df_clean = df_clean.drop_duplicates()\n",
    "print(f\"2.1 去重后: {df_clean.shape}\")\n",
    "\n",
    "# 2.2 删除employee_id缺失的行（关键字段）\n",
    "df_clean = df_clean.dropna(subset=['employee_id'])\n",
    "print(f\"2.2 删除ID缺失后: {df_clean.shape}\")\n",
    "\n",
    "# 2.3 处理年龄异常值\n",
    "# 业务规则: 年龄应在18-65之间\n",
    "print(f\"\\n2.3 年龄异常值: {((df_clean['age'] < 18) | (df_clean['age'] > 65)).sum()}个\")\n",
    "df_clean = df_clean[(df_clean['age'] >= 18) & (df_clean['age'] <= 65)]\n",
    "print(f\"     删除后: {df_clean.shape}\")\n",
    "\n",
    "# 2.4 处理薪资异常值\n",
    "print(f\"\\n2.4 薪资统计:\")\n",
    "print(f\"     最小值: {df_clean['salary'].min():.0f}\")\n",
    "print(f\"     最大值: {df_clean['salary'].max():.0f}\")\n",
    "\n",
    "# 业务规则: 薪资应在3000-100000之间\n",
    "salary_outliers = ((df_clean['salary'] < 3000) | (df_clean['salary'] > 100000)).sum()\n",
    "print(f\"     异常值数量: {salary_outliers}\")\n",
    "df_clean = df_clean[(df_clean['salary'] >= 3000) & (df_clean['salary'] <= 100000)]\n",
    "print(f\"     删除后: {df_clean.shape}\")\n",
    "\n",
    "# 2.5 填充缺失值\n",
    "print(\"\\n2.5 填充缺失值:\")\n",
    "# 姓名缺失用'未知姓名'\n",
    "df_clean['name'].fillna('未知姓名', inplace=True)\n",
    "print(f\"     name缺失: {df_clean['name'].isnull().sum()}\")\n",
    "\n",
    "# 部门缺失用众数\n",
    "dept_mode = df_clean['department'].mode()[0]\n",
    "df_clean['department'].fillna(dept_mode, inplace=True)\n",
    "print(f\"     department缺失: {df_clean['department'].isnull().sum()}\")\n",
    "\n",
    "# 薪资缺失用部门中位数\n",
    "df_clean['salary'] = df_clean.groupby('department')['salary'].transform(\n",
    "    lambda x: x.fillna(x.median())\n",
    ")\n",
    "print(f\"     salary缺失: {df_clean['salary'].isnull().sum()}\")\n",
    "\n",
    "# 邮箱缺失保持\n",
    "print(f\"     email缺失: {df_clean['email'].isnull().sum()} (保留)\")\n",
    "\n",
    "print(f\"\\n✅ 清洗完成: {df_clean.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "步骤3: 清洗前后对比\n",
      "======================================================================\n",
      "      指标       清洗前      清洗后\n",
      "0    总行数    103.00    81.00\n",
      "1    总列数      7.00     7.00\n",
      "2  缺失值总数     30.00     0.00\n",
      "3   重复行数      3.00     0.00\n",
      "4   平均年龄     50.50    39.01\n",
      "5   平均薪资  42601.86 26724.74\n",
      "6  薪资标准差 111070.54 14364.88\n",
      "\n",
      "✅ 数据清洗总结:\n",
      "   删除了 22 行脏数据 (21.4%)\n",
      "   缺失值从 30 减少到 0\n",
      "   数据质量显著提升！\n"
     ]
    }
   ],
   "source": [
    "# 步骤3: 清洗前后对比\n",
    "print(\"=\"*70)\n",
    "print(\"步骤3: 清洗前后对比\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "comparison = pd.DataFrame({\n",
    "    '指标': [\n",
    "        '总行数',\n",
    "        '总列数',\n",
    "        '缺失值总数',\n",
    "        '重复行数',\n",
    "        '平均年龄',\n",
    "        '平均薪资',\n",
    "        '薪资标准差'\n",
    "    ],\n",
    "    '清洗前': [\n",
    "        len(df_messy),\n",
    "        len(df_messy.columns),\n",
    "        df_messy.isnull().sum().sum(),\n",
    "        df_messy.duplicated().sum(),\n",
    "        df_messy['age'].mean(),\n",
    "        df_messy['salary'].mean(),\n",
    "        df_messy['salary'].std()\n",
    "    ],\n",
    "    '清洗后': [\n",
    "        len(df_clean),\n",
    "        len(df_clean.columns),\n",
    "        df_clean.isnull().sum().sum(),\n",
    "        df_clean.duplicated().sum(),\n",
    "        df_clean['age'].mean(),\n",
    "        df_clean['salary'].mean(),\n",
    "        df_clean['salary'].std()\n",
    "    ]\n",
    "})\n",
    "\n",
    "print(comparison.round(2))\n",
    "\n",
    "print(\"\\n✅ 数据清洗总结:\")\n",
    "print(f\"   删除了 {len(df_messy)-len(df_clean)} 行脏数据 ({(len(df_messy)-len(df_clean))/len(df_messy)*100:.1f}%)\")\n",
    "print(f\"   缺失值从 {df_messy.isnull().sum().sum()} 减少到 {df_clean.isnull().sum().sum()}\")\n",
    "print(f\"   数据质量显著提升！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出清洗后的数据\n",
    "df_clean.to_csv('cleaned_employee_data.csv', index=False, encoding='utf-8-sig')\n",
    "df_clean.to_excel('cleaned_employee_data.xlsx', index=False)\n",
    "print(\"\\n✅ 清洗后的数据已导出:\")\n",
    "print(\"   - cleaned_employee_data.csv\")\n",
    "print(\"   - cleaned_employee_data.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 七、本讲总结\n",
    "\n",
    "### 核心知识点\n",
    "\n",
    "#### 1. 缺失值处理\n",
    "\n",
    "**检测方法**:\n",
    "- `isnull()`, `isna()`: 检测缺失\n",
    "- `info()`: 快速查看\n",
    "- 可视化: 热力图、条形图\n",
    "\n",
    "**处理策略**:\n",
    "- **删除**: `dropna()`\n",
    "- **固定值**: `fillna(0)`, `fillna('未知')`\n",
    "- **统计值**: 均值/中位数/众数\n",
    "- **前后向**: `ffill()`, `bfill()`\n",
    "- **插值**: `interpolate()`\n",
    "- **分组填充**: `groupby().transform()`\n",
    "\n",
    "**决策原则**:\n",
    "- 缺失 >50% → 删除列\n",
    "- 缺失 <5% → 删除行\n",
    "- 分类变量 → 众数或新类别\n",
    "- 数值变量 → 中位数（有异常）/均值（无异常）\n",
    "- 时间序列 → 插值或前后填充\n",
    "\n",
    "#### 2. 异常值处理\n",
    "\n",
    "**检测方法**:\n",
    "1. **业务规则法** ⭐推荐首选\n",
    "   - 基于业务知识定义合理范围\n",
    "   \n",
    "2. **3σ原则**\n",
    "   - 适用于正态分布\n",
    "   - 界限: μ ± 3σ\n",
    "\n",
    "3. **IQR法** ⭐推荐\n",
    "   - 不假设分布\n",
    "   - 界限: Q1-1.5×IQR, Q3+1.5×IQR\n",
    "   \n",
    "4. **Z-Score法**\n",
    "   - 标准化距离\n",
    "   - |Z| > 3 为异常\n",
    "\n",
    "**处理策略**:\n",
    "- **删除**: 确认错误且数量少\n",
    "- **边界替换**: `clip()` 限制极值\n",
    "- **统计值替换**: 中位数/均值\n",
    "- **保留标记**: 不确定时做标记\n",
    "\n",
    "### Excel vs Pandas 对比\n",
    "\n",
    "| 任务 | Excel | Pandas |\n",
    "|------|-------|--------|\n",
    "| 缺失值检查 | 手动查找空白 | `isnull().sum()` |\n",
    "| 缺失值填充 | 手动填充/Ctrl+D | `fillna()` 多种方法 |\n",
    "| 异常值检测 | 条件格式/排序 | IQR自动检测 |\n",
    "| 异常值处理 | 手动删除/替换 | `clip()`, 批量处理 |\n",
    "| 可视化 | 手动创建图表 | 一行代码生成 |\n",
    "| 批量处理 | 需要VBA | 原生支持 |\n",
    "\n",
    "### 最佳实践\n",
    "\n",
    "1. **先理解再处理**: 分析缺失/异常的原因和模式\n",
    "2. **保留原始数据**: 在副本上操作\n",
    "3. **记录处理过程**: 代码就是文档\n",
    "4. **验证结果**: 清洗前后对比统计量\n",
    "5. **业务优先**: 结合业务逻辑，不只看统计\n",
    "\n",
    "### 下节预告\n",
    "**第4讲: 数据格式规范与去重**\n",
    "- 数据类型转换\n",
    "- 日期时间处理\n",
    "- 字符串清洗\n",
    "- 去重策略\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后作业\n",
    "\n",
    "### 作业1: 缺失值处理实战\n",
    "给定一个包含缺失值的销售数据集，完成:\n",
    "1. 完整的缺失值检查报告\n",
    "2. 分析缺失模式（MCAR/MAR/MNAR）\n",
    "3. 对不同列选择合适的填充策略并说明理由\n",
    "4. 对比3种填充方法的效果\n",
    "5. 可视化缺失模式\n",
    "\n",
    "### 作业2: 异常值检测对比\n",
    "1. 用4种方法（业务规则/3σ/IQR/Z-Score）检测同一数据集的异常值\n",
    "2. 对比各方法检出的异常值数量和重叠情况\n",
    "3. 分析哪种方法最适合该数据\n",
    "4. 用3种策略处理异常值并对比效果\n",
    "5. 绘制完整的可视化报告\n",
    "\n",
    "### 作业3: 完整清洗流程\n",
    "提供一份包含多种脏数据的真实数据集，要求:\n",
    "1. 完整的数据质量评估报告\n",
    "2. 设计并实施清洗方案\n",
    "3. 记录每一步的处理逻辑\n",
    "4. 清洗前后对比分析\n",
    "5. 导出清洗后的数据和清洗日志\n",
    "\n",
    "### 作业4: 编写清洗函数\n",
    "编写一个通用的数据清洗函数包，包含:\n",
    "1. 缺失值自动处理函数（智能选择策略）\n",
    "2. 异常值自动检测和处理函数\n",
    "3. 数据质量评分函数\n",
    "4. 清洗报告生成函数\n",
    "5. 可以应用到任意数据集"
   ]
  }
 ],
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